{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://deecamp.chuangxin.com/assets/image/logo_nav_zh.jpg\" width=\"40%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block_1.attributes  block_2.attributes\tblock_3.attributes\r\n",
      "block_1.tuples\t    block_2.tuples\tblock_3.tuples\r\n"
     ]
    }
   ],
   "source": [
    "! ls /data/jupyter_root/ZJ/test1.0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义一个函数，先输出原始feature文件的P & R，再输出通过D-Cube分块的P & R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "名称 | 定义\n",
    " --- | ---\n",
    "TP\t| 真实类别为positive，模型预测的类别也为positive\n",
    "FP\t| 预测为positive，但真实类别为negative，真实类别和预测类别不一致\n",
    "FN\t| 预测为negative，但真实类别为positive，真实类别和预测类别不一致\n",
    "TN\t| 真实类别为negative，模型预测的类别也为negative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准确率（accuracy）计算公式如下所示 不计算这个\n",
    "\n",
    "\\begin{equation}\\label{equ:accuracy} \\mbox{accuracy} = \\frac{TP+TN}{TP+TN+FP+FN} = \\frac{TP+TN }{\\mbox{all data}} \\end{equation}\n",
    "\n",
    "positive class的精确率（precision）计算公式如下\n",
    "\n",
    "\\begin{equation}\\label{equ:precision} \\mbox{precision} = \\frac{TP}{TP+FP} = \\frac{TP}{\\mbox{预测为positive的样本}} \\end{equation}\n",
    "\n",
    "positive class的召回率（recall）计算公式如下\n",
    "\n",
    "\\begin{equation}\\label{equ:recall} \\mbox{recall} = \\frac{TP}{TP+FN} = \\frac{TP}{\\mbox{真实为positive的样本}} \\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_PR(f_count,j,choice):#f_count：the index of feature;j: index of test; choice: index of block\n",
    "    \n",
    "    #准备用于读取数据的 pandas头（防止第一行数据成为标签）\n",
    "    data_path=\"/data/jupyter_root/dcube_data/feature\"#feature文件位置\n",
    "    #try:\n",
    "    cols = pd.read_csv(data_path+str(f_count)+\"/test\"+str(j)+\"/blocks.txt\",nrows=1).columns#先读第一行，用于提取数据维度\n",
    "    #except e:\n",
    "    #    print(e)\n",
    "    #    print(\"false1\")\n",
    "    #    return 0        \n",
    "\n",
    "    user_index=['user_name']#初始化一个等待补全的names\n",
    "    #print(cols.shape[0]-1)\n",
    "    for i in range(1,cols.shape[0]-1):\n",
    "            user_index.append(str(i))\n",
    "    user_index.append('count')#补上最后的count\n",
    "    \n",
    "    #开始读取数据\n",
    "    #result=pd.read_csv(data_path+str(f_count)+\"/test\"+str(j)+\"/block_1.tuples\",sep=',',names=user_index)#读取全部的数据，并给上names\n",
    "    #try:\n",
    "    if(choice=='all'):\n",
    "        result=pd.read_csv(data_path+str(f_count)+\"/test\"+str(j)+\"/blocks.txt\",sep=',',names=user_index)\n",
    "    elif(choice==1 or choice==2 or choice==3 ):\n",
    "        result=pd.read_csv(data_path+str(f_count)+\"/test\"+str(j)+\"/block_\"+str(choice)+\".tuples\",sep=',',names=user_index)\n",
    "    elif(choice==12 or choice==13 or choice==23 or choice ==123 ):\n",
    "        result=pd.read_csv(data_path+str(f_count)+\"/test\"+str(j)+\"/block\"+str(choice)+\".txt\",sep=',',names=user_index)\n",
    "    #except:\n",
    "    #    print(\"false2\")\n",
    "    #    return 0\n",
    "    names = result['user_name'].drop_duplicates()#预测出坏用户的用户列表\n",
    "    #print(names)\n",
    "    dataset = pd.read_csv(open('/data/csv/label_eventsV1.csv','r',encoding = 'gb18030'))#读取总标签数据\n",
    "    data = dataset[dataset['user_name'].isin(names)][['user_name','label']]#预测出坏用户的真实标签分布\n",
    "    data = data.drop_duplicates()#去重\n",
    "    #print(data)\n",
    "\n",
    "    \n",
    "    raw_result=pd.read_csv(data_path+str(f_count)+\".txt\",sep=',',names=user_index)#读取全部的原始数据，并给上names\n",
    "    raw_names = raw_result['user_name'].drop_duplicates()#原始数据中用户的用户列表\n",
    "    #print(raw_names)\n",
    "    raw_data = dataset[dataset['user_name'].isin(raw_names)][['user_name','label']]#原始数据中坏用户的真实标签分布\n",
    "    raw_data = raw_data.drop_duplicates()#去重\n",
    "    #print(raw_data)\n",
    "    \n",
    "    \n",
    "    TP=data.label.sum()\n",
    "    if(TP==0):\n",
    "        return 0\n",
    "    P=raw_data.label.sum()\n",
    "    FP=data.label.count()-data.label.sum()\n",
    "    N=raw_data.label.count()-raw_data.label.sum()\n",
    "    Precision = (TP/P)/(TP/P+FP/N) #精确率计算\n",
    "\n",
    "    #data_label_1 = dataset[dataset['label']==1][['user_name','label']] #原数据中label为1的坏用户\n",
    "    #data_label_1 = data_label_1.drop_duplicates()#去重\n",
    "    Recall = data.label.sum()/raw_data.label.sum() # 召回率计算\n",
    "    print(\"TP\",TP,\"P\",P,\"FP\",FP,\"N\",N)\n",
    "    print('测试特征'+str(f_count)+\"_\"+str(j)+str(choice))\n",
    "    print('\\t\\t精确率 Precision\\t= '+str(Precision))\n",
    "    print('\\t\\t召回率 Recall   \\t= '+str(Recall))\n",
    "#get_PR(1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征1_31\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征1_3all\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征1_312\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征1_313\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 2 N 3871\n",
      "测试特征1_3123\n",
      "\t\t精确率 Precision\t= 0.2589817354653108\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 2078 P 5538 FP 1516 N 3871\n",
      "测试特征2_31\n",
      "\t\t精确率 Precision\t= 0.4893041450171434\n",
      "\t\t召回率 Recall   \t= 0.37522571325388226\n",
      "TP 284 P 5538 FP 298 N 3871\n",
      "测试特征2_32\n",
      "\t\t精确率 Precision\t= 0.39981408799834744\n",
      "\t\t召回率 Recall   \t= 0.05128205128205128\n",
      "TP 2774 P 5538 FP 1818 N 3871\n",
      "测试特征2_33\n",
      "\t\t精确率 Precision\t= 0.5161026226846006\n",
      "\t\t召回率 Recall   \t= 0.5009028530155291\n",
      "TP 2078 P 5538 FP 1516 N 3871\n",
      "测试特征2_3all\n",
      "\t\t精确率 Precision\t= 0.4893041450171434\n",
      "\t\t召回率 Recall   \t= 0.37522571325388226\n",
      "TP 2119 P 5538 FP 1548 N 3871\n",
      "测试特征2_312\n",
      "\t\t精确率 Precision\t= 0.4889667790589273\n",
      "\t\t召回率 Recall   \t= 0.3826291079812207\n",
      "TP 4852 P 5538 FP 3334 N 3871\n",
      "测试特征2_313\n",
      "\t\t精确率 Precision\t= 0.504274309274843\n",
      "\t\t召回率 Recall   \t= 0.8761285662694114\n",
      "TP 3058 P 5538 FP 2116 N 3871\n",
      "测试特征2_323\n",
      "\t\t精确率 Precision\t= 0.502528238541758\n",
      "\t\t召回率 Recall   \t= 0.5521849042975804\n",
      "TP 4893 P 5538 FP 3366 N 3871\n",
      "测试特征2_3123\n",
      "\t\t精确率 Precision\t= 0.5039899061540865\n",
      "\t\t召回率 Recall   \t= 0.8835319609967497\n",
      "TP 1097 P 5538 FP 853 N 3871\n",
      "测试特征3_31\n",
      "\t\t精确率 Precision\t= 0.47338875932079294\n",
      "\t\t召回率 Recall   \t= 0.19808595160707837\n",
      "TP 1732 P 5538 FP 1195 N 3871\n",
      "测试特征3_32\n",
      "\t\t精确率 Precision\t= 0.503252472024357\n",
      "\t\t召回率 Recall   \t= 0.3127482845792705\n",
      "TP 2916 P 5538 FP 2012 N 3871\n",
      "测试特征3_33\n",
      "\t\t精确率 Precision\t= 0.5032407068084535\n",
      "\t\t召回率 Recall   \t= 0.5265438786565547\n",
      "TP 1097 P 5538 FP 853 N 3871\n",
      "测试特征3_3all\n",
      "\t\t精确率 Precision\t= 0.47338875932079294\n",
      "\t\t召回率 Recall   \t= 0.19808595160707837\n",
      "TP 2529 P 5538 FP 1691 N 3871\n",
      "测试特征3_312\n",
      "\t\t精确率 Precision\t= 0.5110940150566052\n",
      "\t\t召回率 Recall   \t= 0.45666305525460454\n",
      "TP 3711 P 5538 FP 2648 N 3871\n",
      "测试特征3_313\n",
      "\t\t精确率 Precision\t= 0.49484423045821196\n",
      "\t\t召回率 Recall   \t= 0.6700975081256771\n",
      "TP 4457 P 5538 FP 3053 N 3871\n",
      "测试特征3_323\n",
      "\t\t精确率 Precision\t= 0.5050574842725798\n",
      "\t\t召回率 Recall   \t= 0.8048031780426147\n",
      "TP 4984 P 5538 FP 3386 N 3871\n",
      "测试特征3_3123\n",
      "\t\t精确率 Precision\t= 0.50711525579941\n",
      "\t\t召回率 Recall   \t= 0.8999638858793788\n",
      "TP 1198 P 5538 FP 971 N 3871\n",
      "测试特征4_31\n",
      "\t\t精确率 Precision\t= 0.4630578812116719\n",
      "\t\t召回率 Recall   \t= 0.2163235825207656\n",
      "TP 180 P 5538 FP 239 N 3871\n",
      "测试特征4_32\n",
      "\t\t精确率 Precision\t= 0.34487878904869523\n",
      "\t\t召回率 Recall   \t= 0.032502708559046585\n",
      "TP 1891 P 5538 FP 1123 N 3871\n",
      "测试特征4_33\n",
      "\t\t精确率 Precision\t= 0.5406554358499575\n",
      "\t\t召回率 Recall   \t= 0.34145901047309496\n",
      "TP 1198 P 5538 FP 971 N 3871\n",
      "测试特征4_3all\n",
      "\t\t精确率 Precision\t= 0.4630578812116719\n",
      "\t\t召回率 Recall   \t= 0.2163235825207656\n",
      "TP 1278 P 5538 FP 1043 N 3871\n",
      "测试特征4_312\n",
      "\t\t精确率 Precision\t= 0.4613459399332592\n",
      "\t\t召回率 Recall   \t= 0.23076923076923078\n",
      "TP 3014 P 5538 FP 1978 N 3871\n",
      "测试特征4_313\n",
      "\t\t精确率 Precision\t= 0.5157601060024778\n",
      "\t\t召回率 Recall   \t= 0.5442397977609246\n",
      "TP 2028 P 5538 FP 1300 N 3871\n",
      "测试特征4_323\n",
      "\t\t精确率 Precision\t= 0.5216278129632125\n",
      "\t\t召回率 Recall   \t= 0.36619718309859156\n",
      "TP 3054 P 5538 FP 2017 N 3871\n",
      "测试特征4_3123\n",
      "\t\t精确率 Precision\t= 0.5141762982022582\n",
      "\t\t召回率 Recall   \t= 0.551462621885157\n",
      "TP 1175 P 5538 FP 986 N 3871\n",
      "测试特征5_31\n",
      "\t\t精确率 Precision\t= 0.45443836795937376\n",
      "\t\t召回率 Recall   \t= 0.2121704586493319\n",
      "TP 2000 P 5538 FP 1293 N 3871\n",
      "测试特征5_32\n",
      "\t\t精确率 Precision\t= 0.5195054780248916\n",
      "\t\t召回率 Recall   \t= 0.36114120621162876\n",
      "TP 63 P 5538 FP 117 N 3871\n",
      "测试特征5_33\n",
      "\t\t精确率 Precision\t= 0.2734557124259519\n",
      "\t\t召回率 Recall   \t= 0.011375947995666305\n",
      "TP 1175 P 5538 FP 986 N 3871\n",
      "测试特征5_3all\n",
      "\t\t精确率 Precision\t= 0.45443836795937376\n",
      "\t\t召回率 Recall   \t= 0.2121704586493319\n",
      "TP 3014 P 5538 FP 2025 N 3871\n",
      "测试特征5_312\n",
      "\t\t精确率 Precision\t= 0.5098931702634654\n",
      "\t\t召回率 Recall   \t= 0.5442397977609246\n",
      "TP 1205 P 5538 FP 1020 N 3871\n",
      "测试特征5_313\n",
      "\t\t精确率 Precision\t= 0.4522847406483754\n",
      "\t\t召回率 Recall   \t= 0.2175875767425063\n",
      "TP 2030 P 5538 FP 1322 N 3871\n",
      "测试特征5_323\n",
      "\t\t精确率 Precision\t= 0.5176849942217613\n",
      "\t\t召回率 Recall   \t= 0.3665583243048032\n",
      "TP 3031 P 5538 FP 2039 N 3871\n",
      "测试特征5_3123\n",
      "\t\t精确率 Precision\t= 0.5095769668972178\n",
      "\t\t召回率 Recall   \t= 0.5473094980137234\n",
      "TP 1 P 5538 FP 9 N 3871\n",
      "测试特征6_31\n",
      "\t\t精确率 Precision\t= 0.07206821439874891\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 32 P 5538 FP 101 N 3871\n",
      "测试特征6_32\n",
      "\t\t精确率 Precision\t= 0.18130882159218983\n",
      "\t\t召回率 Recall   \t= 0.00577825929938606\n",
      "TP 166 P 5538 FP 669 N 3871\n",
      "测试特征6_33\n",
      "\t\t精确率 Precision\t= 0.14780559345721733\n",
      "\t\t召回率 Recall   \t= 0.029974720115565186\n",
      "TP 1 P 5538 FP 9 N 3871\n",
      "测试特征6_3all\n",
      "\t\t精确率 Precision\t= 0.07206821439874891\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 33 P 5538 FP 110 N 3871\n",
      "测试特征6_312\n",
      "\t\t精确率 Precision\t= 0.1733464690340782\n",
      "\t\t召回率 Recall   \t= 0.005958829902491874\n",
      "TP 167 P 5538 FP 678 N 3871\n",
      "测试特征6_313\n",
      "\t\t精确率 Precision\t= 0.14688128589770882\n",
      "\t\t召回率 Recall   \t= 0.030155290718671\n",
      "TP 198 P 5538 FP 770 N 3871\n",
      "测试特征6_323\n",
      "\t\t精确率 Precision\t= 0.15235558820828357\n",
      "\t\t召回率 Recall   \t= 0.035752979414951244\n",
      "TP 199 P 5538 FP 779 N 3871\n",
      "测试特征6_3123\n",
      "\t\t精确率 Precision\t= 0.15150741548070962\n",
      "\t\t召回率 Recall   \t= 0.03593355001805706\n",
      "TP 2077 P 5538 FP 1500 N 3871\n",
      "测试特征7_31\n",
      "\t\t精确率 Precision\t= 0.49183544668899937\n",
      "\t\t召回率 Recall   \t= 0.37504514265077643\n",
      "TP 284 P 5538 FP 297 N 3871\n",
      "测试特征7_32\n",
      "\t\t精确率 Precision\t= 0.4006209573091849\n",
      "\t\t召回率 Recall   \t= 0.05128205128205128\n",
      "TP 2775 P 5538 FP 1832 N 3871\n",
      "测试特征7_33\n",
      "\t\t精确率 Precision\t= 0.5142766002154097\n",
      "\t\t召回率 Recall   \t= 0.5010834236186349\n",
      "TP 2077 P 5538 FP 1500 N 3871\n",
      "测试特征7_3all\n",
      "\t\t精确率 Precision\t= 0.49183544668899937\n",
      "\t\t召回率 Recall   \t= 0.37504514265077643\n",
      "TP 2118 P 5538 FP 1532 N 3871\n",
      "测试特征7_312\n",
      "\t\t精确率 Precision\t= 0.4914452405845138\n",
      "\t\t召回率 Recall   \t= 0.38244853737811485\n",
      "TP 4852 P 5538 FP 3332 N 3871\n",
      "测试特征7_313\n",
      "\t\t精确率 Precision\t= 0.5044243129286793\n",
      "\t\t召回率 Recall   \t= 0.8761285662694114\n",
      "TP 3059 P 5538 FP 2129 N 3871\n",
      "测试特征7_323\n",
      "\t\t精确率 Precision\t= 0.5010787798521068\n",
      "\t\t召回率 Recall   \t= 0.5523654749006862\n",
      "TP 4893 P 5538 FP 3364 N 3871\n",
      "测试特征7_3123\n",
      "\t\t精确率 Precision\t= 0.5041384847501247\n",
      "\t\t召回率 Recall   \t= 0.8835319609967497\n",
      "TP 1096 P 5538 FP 849 N 3871\n",
      "测试特征8_31\n",
      "\t\t精确率 Precision\t= 0.4743332627489581\n",
      "\t\t召回率 Recall   \t= 0.19790538100397256\n",
      "TP 1732 P 5538 FP 1187 N 3871\n",
      "测试特征8_32\n",
      "\t\t精确率 Precision\t= 0.504931625408245\n",
      "\t\t召回率 Recall   \t= 0.3127482845792705\n",
      "TP 42 P 5538 FP 18 N 3871\n",
      "测试特征8_33\n",
      "\t\t精确率 Precision\t= 0.619912607810391\n",
      "\t\t召回率 Recall   \t= 0.007583965330444204\n",
      "TP 1096 P 5538 FP 849 N 3871\n",
      "测试特征8_3all\n",
      "\t\t精确率 Precision\t= 0.4743332627489581\n",
      "\t\t召回率 Recall   \t= 0.19790538100397256\n",
      "TP 2528 P 5538 FP 1682 N 3871\n",
      "测试特征8_312\n",
      "\t\t精确率 Precision\t= 0.5123285909849659\n",
      "\t\t召回率 Recall   \t= 0.4564824846514987\n",
      "TP 1138 P 5538 FP 867 N 3871\n",
      "测试特征8_313\n",
      "\t\t精确率 Precision\t= 0.4784803235576395\n",
      "\t\t召回率 Recall   \t= 0.20548934633441676\n",
      "TP 1774 P 5538 FP 1205 N 3871\n",
      "测试特征8_323\n",
      "\t\t精确率 Precision\t= 0.5071587017382886\n",
      "\t\t召回率 Recall   \t= 0.3203322499097147\n",
      "TP 2570 P 5538 FP 1700 N 3871\n",
      "测试特征8_3123\n",
      "\t\t精确率 Precision\t= 0.513785778804704\n",
      "\t\t召回率 Recall   \t= 0.46406644998194296\n",
      "TP 1197 P 5538 FP 966 N 3871\n",
      "测试特征9_31\n",
      "\t\t精确率 Precision\t= 0.46413403590698266\n",
      "\t\t召回率 Recall   \t= 0.2161430119176598\n",
      "TP 207 P 5538 FP 256 N 3871\n",
      "测试特征9_32\n",
      "\t\t精确率 Precision\t= 0.36110318721059925\n",
      "\t\t召回率 Recall   \t= 0.03737811484290358\n",
      "TP 1850 P 5538 FP 1093 N 3871\n",
      "测试特征9_33\n",
      "\t\t精确率 Precision\t= 0.5419359691681428\n",
      "\t\t召回率 Recall   \t= 0.3340556157457566\n",
      "TP 1197 P 5538 FP 966 N 3871\n",
      "测试特征9_3all\n",
      "\t\t精确率 Precision\t= 0.46413403590698266\n",
      "\t\t召回率 Recall   \t= 0.2161430119176598\n",
      "TP 1299 P 5538 FP 1047 N 3871\n",
      "测试特征9_312\n",
      "\t\t精确率 Precision\t= 0.4644464179578016\n",
      "\t\t召回率 Recall   \t= 0.23456121343445288\n",
      "TP 2984 P 5538 FP 1963 N 3871\n",
      "测试特征9_313\n",
      "\t\t精确率 Precision\t= 0.5151629026965201\n",
      "\t\t召回率 Recall   \t= 0.5388226796677501\n",
      "TP 2010 P 5538 FP 1292 N 3871\n",
      "测试特征9_323\n",
      "\t\t精确率 Precision\t= 0.5209434295008921\n",
      "\t\t召回率 Recall   \t= 0.3629469122426869\n",
      "TP 3041 P 5538 FP 2005 N 3871\n",
      "测试特征9_3123\n",
      "\t\t精确率 Precision\t= 0.5146012959510524\n",
      "\t\t召回率 Recall   \t= 0.5491152040447815\n",
      "TP 1172 P 5538 FP 979 N 3871\n",
      "测试特征10_31\n",
      "\t\t精确率 Precision\t= 0.45557118260816826\n",
      "\t\t召回率 Recall   \t= 0.21162874684001445\n",
      "TP 1972 P 5538 FP 1290 N 3871\n",
      "测试特征10_32\n",
      "\t\t精确率 Precision\t= 0.5165653062682843\n",
      "\t\t召回率 Recall   \t= 0.35608522932466596\n",
      "TP 89 P 5538 FP 138 N 3871\n",
      "测试特征10_33\n",
      "\t\t精确率 Precision\t= 0.3107237525061713\n",
      "\t\t召回率 Recall   \t= 0.01607078367641748\n",
      "TP 1172 P 5538 FP 979 N 3871\n",
      "测试特征10_3all\n",
      "\t\t精确率 Precision\t= 0.45557118260816826\n",
      "\t\t召回率 Recall   \t= 0.21162874684001445\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 2987 P 5538 FP 2021 N 3871\n",
      "测试特征10_312\n",
      "\t\t精确率 Precision\t= 0.5081384180821996\n",
      "\t\t召回率 Recall   \t= 0.5393643914770675\n",
      "TP 1216 P 5538 FP 1022 N 3871\n",
      "测试特征10_313\n",
      "\t\t精确率 Precision\t= 0.4540511925758071\n",
      "\t\t召回率 Recall   \t= 0.2195738533766703\n",
      "TP 2010 P 5538 FP 1331 N 3871\n",
      "测试特征10_323\n",
      "\t\t精确率 Precision\t= 0.5135176125748327\n",
      "\t\t召回率 Recall   \t= 0.3629469122426869\n",
      "TP 3008 P 5538 FP 2039 N 3871\n",
      "测试特征10_3123\n",
      "\t\t精确率 Precision\t= 0.5076732378645751\n",
      "\t\t召回率 Recall   \t= 0.5431563741422897\n",
      "TP 257 P 5538 FP 249 N 3871\n",
      "测试特征12_31\n",
      "\t\t精确率 Precision\t= 0.41909311153508977\n",
      "\t\t召回率 Recall   \t= 0.046406644998194295\n",
      "TP 1553 P 5538 FP 887 N 3871\n",
      "测试特征12_32\n",
      "\t\t精确率 Precision\t= 0.5503236078718996\n",
      "\t\t召回率 Recall   \t= 0.2804261466233297\n",
      "TP 2165 P 5538 FP 1429 N 3871\n",
      "测试特征12_33\n",
      "\t\t精确率 Precision\t= 0.5143273040863992\n",
      "\t\t召回率 Recall   \t= 0.3909353557240881\n",
      "TP 257 P 5538 FP 249 N 3871\n",
      "测试特征12_3all\n",
      "\t\t精确率 Precision\t= 0.41909311153508977\n",
      "\t\t召回率 Recall   \t= 0.046406644998194295\n",
      "TP 1773 P 5538 FP 1076 N 3871\n",
      "测试特征12_312\n",
      "\t\t精确率 Precision\t= 0.5352668436569751\n",
      "\t\t召回率 Recall   \t= 0.32015167930660887\n",
      "TP 2422 P 5538 FP 1678 N 3871\n",
      "测试特征12_313\n",
      "\t\t精确率 Precision\t= 0.5022176064420559\n",
      "\t\t召回率 Recall   \t= 0.4373420007222824\n",
      "TP 3651 P 5538 FP 2266 N 3871\n",
      "测试特征12_323\n",
      "\t\t精确率 Precision\t= 0.5296811585012576\n",
      "\t\t召回率 Recall   \t= 0.6592632719393283\n",
      "TP 3871 P 5538 FP 2455 N 3871\n",
      "测试特征12_3123\n",
      "\t\t精确率 Precision\t= 0.5242972368051413\n",
      "\t\t召回率 Recall   \t= 0.6989888046226075\n",
      "TP 583 P 5538 FP 431 N 3871\n",
      "测试特征14_33\n",
      "\t\t精确率 Precision\t= 0.4859933014203633\n",
      "\t\t召回率 Recall   \t= 0.10527266161068978\n",
      "TP 583 P 5538 FP 431 N 3871\n",
      "测试特征14_313\n",
      "\t\t精确率 Precision\t= 0.4859933014203633\n",
      "\t\t召回率 Recall   \t= 0.10527266161068978\n",
      "TP 583 P 5538 FP 431 N 3871\n",
      "测试特征14_323\n",
      "\t\t精确率 Precision\t= 0.4859933014203633\n",
      "\t\t召回率 Recall   \t= 0.10527266161068978\n",
      "TP 583 P 5538 FP 431 N 3871\n",
      "测试特征14_3123\n",
      "\t\t精确率 Precision\t= 0.4859933014203633\n",
      "\t\t召回率 Recall   \t= 0.10527266161068978\n",
      "TP 677 P 5538 FP 486 N 3871\n",
      "测试特征15_32\n",
      "\t\t精确率 Precision\t= 0.49333591860899617\n",
      "\t\t召回率 Recall   \t= 0.12224629830263634\n",
      "TP 677 P 5538 FP 486 N 3871\n",
      "测试特征15_312\n",
      "\t\t精确率 Precision\t= 0.49333591860899617\n",
      "\t\t召回率 Recall   \t= 0.12224629830263634\n",
      "TP 677 P 5538 FP 486 N 3871\n",
      "测试特征15_323\n",
      "\t\t精确率 Precision\t= 0.49333591860899617\n",
      "\t\t召回率 Recall   \t= 0.12224629830263634\n",
      "TP 677 P 5538 FP 486 N 3871\n",
      "测试特征15_3123\n",
      "\t\t精确率 Precision\t= 0.49333591860899617\n",
      "\t\t召回率 Recall   \t= 0.12224629830263634\n",
      "TP 191 P 5538 FP 173 N 3871\n",
      "测试特征17_33\n",
      "\t\t精确率 Precision\t= 0.4355754417694933\n",
      "\t\t召回率 Recall   \t= 0.034488985193210545\n",
      "TP 191 P 5538 FP 174 N 3871\n",
      "测试特征17_313\n",
      "\t\t精确率 Precision\t= 0.43415896787559166\n",
      "\t\t召回率 Recall   \t= 0.034488985193210545\n",
      "TP 191 P 5538 FP 173 N 3871\n",
      "测试特征17_323\n",
      "\t\t精确率 Precision\t= 0.4355754417694933\n",
      "\t\t召回率 Recall   \t= 0.034488985193210545\n",
      "TP 191 P 5538 FP 174 N 3871\n",
      "测试特征17_3123\n",
      "\t\t精确率 Precision\t= 0.43415896787559166\n",
      "\t\t召回率 Recall   \t= 0.034488985193210545\n",
      "TP 507 P 5538 FP 376 N 3871\n",
      "测试特征20_32\n",
      "\t\t精确率 Precision\t= 0.4852046473509136\n",
      "\t\t召回率 Recall   \t= 0.09154929577464789\n",
      "TP 507 P 5538 FP 376 N 3871\n",
      "测试特征20_312\n",
      "\t\t精确率 Precision\t= 0.4852046473509136\n",
      "\t\t召回率 Recall   \t= 0.09154929577464789\n",
      "TP 507 P 5538 FP 377 N 3871\n",
      "测试特征20_323\n",
      "\t\t精确率 Precision\t= 0.48454124421078987\n",
      "\t\t召回率 Recall   \t= 0.09154929577464789\n",
      "TP 507 P 5538 FP 377 N 3871\n",
      "测试特征20_3123\n",
      "\t\t精确率 Precision\t= 0.48454124421078987\n",
      "\t\t召回率 Recall   \t= 0.09154929577464789\n",
      "TP 354 P 5538 FP 323 N 3871\n",
      "测试特征22_33\n",
      "\t\t精确率 Precision\t= 0.43377244462677444\n",
      "\t\t召回率 Recall   \t= 0.06392199349945829\n",
      "TP 354 P 5538 FP 324 N 3871\n",
      "测试特征22_313\n",
      "\t\t精确率 Precision\t= 0.4330133607360823\n",
      "\t\t召回率 Recall   \t= 0.06392199349945829\n",
      "TP 354 P 5538 FP 323 N 3871\n",
      "测试特征22_323\n",
      "\t\t精确率 Precision\t= 0.43377244462677444\n",
      "\t\t召回率 Recall   \t= 0.06392199349945829\n",
      "TP 354 P 5538 FP 324 N 3871\n",
      "测试特征22_3123\n",
      "\t\t精确率 Precision\t= 0.4330133607360823\n",
      "\t\t召回率 Recall   \t= 0.06392199349945829\n",
      "TP 1 P 5538 FP 5 N 3871\n",
      "测试特征25_32\n",
      "\t\t精确率 Precision\t= 0.12265137353062325\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 513 P 5538 FP 371 N 3871\n",
      "测试特征25_33\n",
      "\t\t精确率 Precision\t= 0.491489129474379\n",
      "\t\t召回率 Recall   \t= 0.09263271939328277\n",
      "TP 1 P 5538 FP 6 N 3871\n",
      "测试特征25_312\n",
      "\t\t精确率 Precision\t= 0.10434243510606755\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 513 P 5538 FP 371 N 3871\n",
      "测试特征25_313\n",
      "\t\t精确率 Precision\t= 0.491489129474379\n",
      "\t\t召回率 Recall   \t= 0.09263271939328277\n",
      "TP 513 P 5538 FP 373 N 3871\n",
      "测试特征25_323\n",
      "\t\t精确率 Precision\t= 0.4901454943691184\n",
      "\t\t召回率 Recall   \t= 0.09263271939328277\n",
      "TP 513 P 5538 FP 373 N 3871\n",
      "测试特征25_3123\n",
      "\t\t精确率 Precision\t= 0.4901454943691184\n",
      "\t\t召回率 Recall   \t= 0.09263271939328277\n",
      "TP 276 P 5538 FP 269 N 3871\n",
      "测试特征27_33\n",
      "\t\t精确率 Precision\t= 0.41764922493802087\n",
      "\t\t召回率 Recall   \t= 0.04983748645720477\n",
      "TP 276 P 5538 FP 270 N 3871\n",
      "测试特征27_313\n",
      "\t\t精确率 Precision\t= 0.4167470206611183\n",
      "\t\t召回率 Recall   \t= 0.04983748645720477\n",
      "TP 276 P 5538 FP 269 N 3871\n",
      "测试特征27_323\n",
      "\t\t精确率 Precision\t= 0.41764922493802087\n",
      "\t\t召回率 Recall   \t= 0.04983748645720477\n",
      "TP 276 P 5538 FP 270 N 3871\n",
      "测试特征27_3123\n",
      "\t\t精确率 Precision\t= 0.4167470206611183\n",
      "\t\t召回率 Recall   \t= 0.04983748645720477\n",
      "TP 1 P 5538 FP 4 N 3871\n",
      "测试特征30_33\n",
      "\t\t精确率 Precision\t= 0.14875302616915806\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 4 N 3871\n",
      "测试特征30_313\n",
      "\t\t精确率 Precision\t= 0.14875302616915806\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 5 N 3871\n",
      "测试特征30_323\n",
      "\t\t精确率 Precision\t= 0.12265137353062325\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 5 N 3871\n",
      "测试特征30_3123\n",
      "\t\t精确率 Precision\t= 0.12265137353062325\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1903 P 5538 FP 1183 N 3871\n",
      "测试特征32_31\n",
      "\t\t精确率 Precision\t= 0.5292808209704766\n",
      "\t\t召回率 Recall   \t= 0.34362585771036475\n",
      "TP 143 P 5538 FP 123 N 3871\n",
      "测试特征32_32\n",
      "\t\t精确率 Precision\t= 0.44832015498162753\n",
      "\t\t召回率 Recall   \t= 0.025821596244131457\n",
      "TP 2132 P 5538 FP 1278 N 3871\n",
      "测试特征32_33\n",
      "\t\t精确率 Precision\t= 0.5383355154705615\n",
      "\t\t召回率 Recall   \t= 0.38497652582159625\n",
      "TP 1903 P 5538 FP 1183 N 3871\n",
      "测试特征32_3all\n",
      "\t\t精确率 Precision\t= 0.5292808209704766\n",
      "\t\t召回率 Recall   \t= 0.34362585771036475\n",
      "TP 1924 P 5538 FP 1193 N 3871\n",
      "测试特征32_312\n",
      "\t\t精确率 Precision\t= 0.5299178819120065\n",
      "\t\t召回率 Recall   \t= 0.3474178403755869\n",
      "TP 4035 P 5538 FP 2461 N 3871\n",
      "测试特征32_313\n",
      "\t\t精确率 Precision\t= 0.534026818398193\n",
      "\t\t召回率 Recall   \t= 0.728602383531961\n",
      "TP 2275 P 5538 FP 1401 N 3871\n",
      "测试特征32_323\n",
      "\t\t精确率 Precision\t= 0.5316260297225586\n",
      "\t\t召回率 Recall   \t= 0.4107981220657277\n",
      "TP 4056 P 5538 FP 2471 N 3871\n",
      "测试特征32_3123\n",
      "\t\t精确率 Precision\t= 0.5343094446199298\n",
      "\t\t召回率 Recall   \t= 0.7323943661971831\n",
      "TP 1077 P 5538 FP 699 N 3871\n",
      "测试特征33_31\n",
      "\t\t精确率 Precision\t= 0.518532351906294\n",
      "\t\t召回率 Recall   \t= 0.19447453954496208\n",
      "TP 1987 P 5538 FP 1239 N 3871\n",
      "测试特征33_33\n",
      "\t\t精确率 Precision\t= 0.5285192134627715\n",
      "\t\t召回率 Recall   \t= 0.35879378837125314\n",
      "TP 1077 P 5538 FP 699 N 3871\n",
      "测试特征33_3all\n",
      "\t\t精确率 Precision\t= 0.518532351906294\n",
      "\t\t召回率 Recall   \t= 0.19447453954496208\n",
      "TP 1077 P 5538 FP 699 N 3871\n",
      "测试特征33_312\n",
      "\t\t精确率 Precision\t= 0.518532351906294\n",
      "\t\t召回率 Recall   \t= 0.19447453954496208\n",
      "TP 2758 P 5538 FP 1658 N 3871\n",
      "测试特征33_313\n",
      "\t\t精确率 Precision\t= 0.5376220489427502\n",
      "\t\t召回率 Recall   \t= 0.49801372336583605\n",
      "TP 1987 P 5538 FP 1239 N 3871\n",
      "测试特征33_323\n",
      "\t\t精确率 Precision\t= 0.5285192134627715\n",
      "\t\t召回率 Recall   \t= 0.35879378837125314\n",
      "TP 2758 P 5538 FP 1658 N 3871\n",
      "测试特征33_3123\n",
      "\t\t精确率 Precision\t= 0.5376220489427502\n",
      "\t\t召回率 Recall   \t= 0.49801372336583605\n",
      "TP 1270 P 5538 FP 817 N 3871\n",
      "测试特征34_31\n",
      "\t\t精确率 Precision\t= 0.5207412234411034\n",
      "\t\t召回率 Recall   \t= 0.22932466594438425\n",
      "TP 1143 P 5538 FP 733 N 3871\n",
      "测试特征34_33\n",
      "\t\t精确率 Precision\t= 0.5215230435694309\n",
      "\t\t召回率 Recall   \t= 0.20639219934994582\n",
      "TP 1270 P 5538 FP 817 N 3871\n",
      "测试特征34_3all\n",
      "\t\t精确率 Precision\t= 0.5207412234411034\n",
      "\t\t召回率 Recall   \t= 0.22932466594438425\n",
      "TP 1270 P 5538 FP 817 N 3871\n",
      "测试特征34_312\n",
      "\t\t精确率 Precision\t= 0.5207412234411034\n",
      "\t\t召回率 Recall   \t= 0.22932466594438425\n",
      "TP 2283 P 5538 FP 1383 N 3871\n",
      "测试特征34_313\n",
      "\t\t精确率 Precision\t= 0.5357177474776994\n",
      "\t\t召回率 Recall   \t= 0.4122426868905742\n",
      "TP 1143 P 5538 FP 733 N 3871\n",
      "测试特征34_323\n",
      "\t\t精确率 Precision\t= 0.5215230435694309\n",
      "\t\t召回率 Recall   \t= 0.20639219934994582\n",
      "TP 2283 P 5538 FP 1383 N 3871\n",
      "测试特征34_3123\n",
      "\t\t精确率 Precision\t= 0.5357177474776994\n",
      "\t\t召回率 Recall   \t= 0.4122426868905742\n",
      "TP 1270 P 5538 FP 844 N 3871\n",
      "测试特征35_31\n",
      "\t\t精确率 Precision\t= 0.5126221006727463\n",
      "\t\t召回率 Recall   \t= 0.22932466594438425\n",
      "TP 2344 P 5538 FP 1394 N 3871\n",
      "测试特征35_33\n",
      "\t\t精确率 Precision\t= 0.5403026248815322\n",
      "\t\t召回率 Recall   \t= 0.4232574936800289\n",
      "TP 1270 P 5538 FP 844 N 3871\n",
      "测试特征35_3all\n",
      "\t\t精确率 Precision\t= 0.5126221006727463\n",
      "\t\t召回率 Recall   \t= 0.22932466594438425\n",
      "TP 1270 P 5538 FP 844 N 3871\n",
      "测试特征35_312\n",
      "\t\t精确率 Precision\t= 0.5126221006727463\n",
      "\t\t召回率 Recall   \t= 0.22932466594438425\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 3401 P 5538 FP 2009 N 3871\n",
      "测试特征35_313\n",
      "\t\t精确率 Precision\t= 0.5419789122054638\n",
      "\t\t召回率 Recall   \t= 0.6141206211628747\n",
      "TP 2344 P 5538 FP 1394 N 3871\n",
      "测试特征35_323\n",
      "\t\t精确率 Precision\t= 0.5403026248815322\n",
      "\t\t召回率 Recall   \t= 0.4232574936800289\n",
      "TP 3401 P 5538 FP 2009 N 3871\n",
      "测试特征35_3123\n",
      "\t\t精确率 Precision\t= 0.5419789122054638\n",
      "\t\t召回率 Recall   \t= 0.6141206211628747\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征37_31\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 248 P 5538 FP 201 N 3871\n",
      "测试特征37_32\n",
      "\t\t精确率 Precision\t= 0.4630682064842515\n",
      "\t\t召回率 Recall   \t= 0.04478150957024196\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征37_3all\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 248 P 5538 FP 201 N 3871\n",
      "测试特征37_312\n",
      "\t\t精确率 Precision\t= 0.4630682064842515\n",
      "\t\t召回率 Recall   \t= 0.04478150957024196\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征37_313\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 248 P 5538 FP 201 N 3871\n",
      "测试特征37_323\n",
      "\t\t精确率 Precision\t= 0.4630682064842515\n",
      "\t\t召回率 Recall   \t= 0.04478150957024196\n",
      "TP 248 P 5538 FP 201 N 3871\n",
      "测试特征37_3123\n",
      "\t\t精确率 Precision\t= 0.4630682064842515\n",
      "\t\t召回率 Recall   \t= 0.04478150957024196\n",
      "TP 2 P 5538 FP 1 N 3871\n",
      "测试特征38_33\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征38_313\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征38_323\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 2 P 5538 FP 3 N 3871\n",
      "测试特征38_3123\n",
      "\t\t精确率 Precision\t= 0.3178682870750534\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征39_32\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征39_33\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 2 N 3871\n",
      "测试特征39_312\n",
      "\t\t精确率 Precision\t= 0.2589817354653108\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征39_313\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 2 P 5538 FP 1 N 3871\n",
      "测试特征39_323\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征39_3123\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 1 P 5538 FP 2 N 3871\n",
      "测试特征40_31\n",
      "\t\t精确率 Precision\t= 0.2589817354653108\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 3 P 5538 FP 1 N 3871\n",
      "测试特征40_32\n",
      "\t\t精确率 Precision\t= 0.6771033758964492\n",
      "\t\t召回率 Recall   \t= 0.0005417118093174431\n",
      "TP 533 P 5538 FP 349 N 3871\n",
      "测试特征40_33\n",
      "\t\t精确率 Precision\t= 0.5163264310229841\n",
      "\t\t召回率 Recall   \t= 0.09624413145539906\n",
      "TP 1 P 5538 FP 2 N 3871\n",
      "测试特征40_3all\n",
      "\t\t精确率 Precision\t= 0.2589817354653108\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 4 P 5538 FP 3 N 3871\n",
      "测试特征40_312\n",
      "\t\t精确率 Precision\t= 0.48239765717490185\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 533 P 5538 FP 349 N 3871\n",
      "测试特征40_313\n",
      "\t\t精确率 Precision\t= 0.5163264310229841\n",
      "\t\t召回率 Recall   \t= 0.09624413145539906\n",
      "TP 533 P 5538 FP 349 N 3871\n",
      "测试特征40_323\n",
      "\t\t精确率 Precision\t= 0.5163264310229841\n",
      "\t\t召回率 Recall   \t= 0.09624413145539906\n",
      "TP 533 P 5538 FP 349 N 3871\n",
      "测试特征40_3123\n",
      "\t\t精确率 Precision\t= 0.5163264310229841\n",
      "\t\t召回率 Recall   \t= 0.09624413145539906\n",
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征41_31\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征41_32\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征41_3all\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 2 P 5538 FP 0 N 3871\n",
      "测试特征41_312\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征41_313\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征41_323\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 2 P 5538 FP 1 N 3871\n",
      "测试特征41_3123\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 963 P 5538 FP 530 N 3871\n",
      "测试特征42_31\n",
      "\t\t精确率 Precision\t= 0.55948096575777\n",
      "\t\t召回率 Recall   \t= 0.17388949079089924\n",
      "TP 268 P 5538 FP 223 N 3871\n",
      "测试特征42_32\n",
      "\t\t精确率 Precision\t= 0.45653365909729005\n",
      "\t\t召回率 Recall   \t= 0.048392921632358255\n",
      "TP 1849 P 5538 FP 903 N 3871\n",
      "测试特征42_33\n",
      "\t\t精确率 Precision\t= 0.5886911098457653\n",
      "\t\t召回率 Recall   \t= 0.33387504514265076\n",
      "TP 963 P 5538 FP 530 N 3871\n",
      "测试特征42_3all\n",
      "\t\t精确率 Precision\t= 0.55948096575777\n",
      "\t\t召回率 Recall   \t= 0.17388949079089924\n",
      "TP 1148 P 5538 FP 644 N 3871\n",
      "测试特征42_312\n",
      "\t\t精确率 Precision\t= 0.554768687627803\n",
      "\t\t召回率 Recall   \t= 0.2072950523654749\n",
      "TP 2710 P 5538 FP 1367 N 3871\n",
      "测试特征42_313\n",
      "\t\t精确率 Precision\t= 0.580836810835544\n",
      "\t\t召回率 Recall   \t= 0.48934633441675696\n",
      "TP 2042 P 5538 FP 1070 N 3871\n",
      "测试特征42_323\n",
      "\t\t精确率 Precision\t= 0.5715432889749869\n",
      "\t\t召回率 Recall   \t= 0.36872517154207296\n",
      "TP 2820 P 5538 FP 1425 N 3871\n",
      "测试特征42_3123\n",
      "\t\t精确率 Precision\t= 0.5804070317372462\n",
      "\t\t召回率 Recall   \t= 0.5092091007583965\n",
      "TP 1067 P 5538 FP 586 N 3871\n",
      "测试特征43_31\n",
      "\t\t精确率 Precision\t= 0.5600009490721125\n",
      "\t\t召回率 Recall   \t= 0.19266883351390393\n",
      "TP 259 P 5538 FP 181 N 3871\n",
      "测试特征43_33\n",
      "\t\t精确率 Precision\t= 0.500052619319919\n",
      "\t\t召回率 Recall   \t= 0.04676778620440592\n",
      "TP 1067 P 5538 FP 586 N 3871\n",
      "测试特征43_3all\n",
      "\t\t精确率 Precision\t= 0.5600009490721125\n",
      "\t\t召回率 Recall   \t= 0.19266883351390393\n",
      "TP 1067 P 5538 FP 586 N 3871\n",
      "测试特征43_312\n",
      "\t\t精确率 Precision\t= 0.5600009490721125\n",
      "\t\t召回率 Recall   \t= 0.19266883351390393\n",
      "TP 1211 P 5538 FP 658 N 3871\n",
      "测试特征43_313\n",
      "\t\t精确率 Precision\t= 0.5626382581883714\n",
      "\t\t召回率 Recall   \t= 0.2186710003611412\n",
      "TP 259 P 5538 FP 181 N 3871\n",
      "测试特征43_323\n",
      "\t\t精确率 Precision\t= 0.500052619319919\n",
      "\t\t召回率 Recall   \t= 0.04676778620440592\n",
      "TP 1211 P 5538 FP 658 N 3871\n",
      "测试特征43_3123\n",
      "\t\t精确率 Precision\t= 0.5626382581883714\n",
      "\t\t召回率 Recall   \t= 0.2186710003611412\n",
      "TP 848 P 5538 FP 496 N 3871\n",
      "测试特征44_31\n",
      "\t\t精确率 Precision\t= 0.5444285520949155\n",
      "\t\t召回率 Recall   \t= 0.1531238714337306\n",
      "TP 1282 P 5538 FP 667 N 3871\n",
      "测试特征44_32\n",
      "\t\t精确率 Precision\t= 0.5732848547467628\n",
      "\t\t召回率 Recall   \t= 0.23149151318165404\n",
      "TP 2 P 5538 FP 1 N 3871\n",
      "测试特征44_33\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 848 P 5538 FP 496 N 3871\n",
      "测试特征44_3all\n",
      "\t\t精确率 Precision\t= 0.5444285520949155\n",
      "\t\t召回率 Recall   \t= 0.1531238714337306\n",
      "TP 2033 P 5538 FP 1053 N 3871\n",
      "测试特征44_312\n",
      "\t\t精确率 Precision\t= 0.5743810951068212\n",
      "\t\t召回率 Recall   \t= 0.3671000361141206\n",
      "TP 849 P 5538 FP 497 N 3871\n",
      "测试特征44_313\n",
      "\t\t精确率 Precision\t= 0.544221306487229\n",
      "\t\t召回率 Recall   \t= 0.1533044420368364\n",
      "TP 1284 P 5538 FP 668 N 3871\n",
      "测试特征44_323\n",
      "\t\t精确率 Precision\t= 0.5732997083652258\n",
      "\t\t召回率 Recall   \t= 0.23185265438786565\n",
      "TP 2034 P 5538 FP 1054 N 3871\n",
      "测试特征44_3123\n",
      "\t\t精确率 Precision\t= 0.5742692586924661\n",
      "\t\t召回率 Recall   \t= 0.36728060671722645\n",
      "TP 1217 P 5538 FP 666 N 3871\n",
      "测试特征45_31\n",
      "\t\t精确率 Precision\t= 0.5608799050874982\n",
      "\t\t召回率 Recall   \t= 0.2197544239797761\n",
      "TP 228 P 5538 FP 153 N 3871\n",
      "测试特征45_32\n",
      "\t\t精确率 Precision\t= 0.5101953752293482\n",
      "\t\t召回率 Recall   \t= 0.041170097508125676\n",
      "TP 1526 P 5538 FP 774 N 3871\n",
      "测试特征45_33\n",
      "\t\t精确率 Precision\t= 0.5794979535114236\n",
      "\t\t召回率 Recall   \t= 0.27555074033947274\n",
      "TP 1217 P 5538 FP 666 N 3871\n",
      "测试特征45_3all\n",
      "\t\t精确率 Precision\t= 0.5608799050874982\n",
      "\t\t召回率 Recall   \t= 0.2197544239797761\n",
      "TP 1356 P 5538 FP 731 N 3871\n",
      "测试特征45_312\n",
      "\t\t精确率 Precision\t= 0.5645774055715207\n",
      "\t\t召回率 Recall   \t= 0.24485373781148428\n",
      "TP 2671 P 5538 FP 1360 N 3871\n",
      "测试特征45_313\n",
      "\t\t精确率 Precision\t= 0.5785558163922677\n",
      "\t\t召回率 Recall   \t= 0.4823040808956302\n",
      "TP 1697 P 5538 FP 875 N 3871\n",
      "测试特征45_323\n",
      "\t\t精确率 Precision\t= 0.5754867108483458\n",
      "\t\t召回率 Recall   \t= 0.306428313470567\n",
      "TP 2771 P 5538 FP 1396 N 3871\n",
      "测试特征45_3123\n",
      "\t\t精确率 Precision\t= 0.581145294761954\n",
      "\t\t召回率 Recall   \t= 0.5003611412062117\n",
      "TP 1 P 5538 FP 0 N 3871\n",
      "测试特征46_32\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 1 N 3871\n",
      "测试特征46_33\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 2 N 3871\n",
      "测试特征46_312\n",
      "\t\t精确率 Precision\t= 0.2589817354653108\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 1 P 5538 FP 3 N 3871\n",
      "测试特征46_313\n",
      "\t\t精确率 Precision\t= 0.18896753722235782\n",
      "\t\t召回率 Recall   \t= 0.00018057060310581438\n",
      "TP 2 P 5538 FP 1 N 3871\n",
      "测试特征46_323\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 2 P 5538 FP 3 N 3871\n",
      "测试特征46_3123\n",
      "\t\t精确率 Precision\t= 0.3178682870750534\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 975 P 5538 FP 550 N 3871\n",
      "测试特征47_31\n",
      "\t\t精确率 Precision\t= 0.5533952823445317\n",
      "\t\t召回率 Recall   \t= 0.176056338028169\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 220 P 5538 FP 181 N 3871\n",
      "测试特征47_32\n",
      "\t\t精确率 Precision\t= 0.45934245883760394\n",
      "\t\t召回率 Recall   \t= 0.03972553268327916\n",
      "TP 1741 P 5538 FP 820 N 3871\n",
      "测试特征47_33\n",
      "\t\t精确率 Precision\t= 0.5974352716719747\n",
      "\t\t召回率 Recall   \t= 0.31437342000722285\n",
      "TP 975 P 5538 FP 550 N 3871\n",
      "测试特征47_3all\n",
      "\t\t精确率 Precision\t= 0.5533952823445317\n",
      "\t\t召回率 Recall   \t= 0.176056338028169\n",
      "TP 1124 P 5538 FP 641 N 3871\n",
      "测试特征47_312\n",
      "\t\t精确率 Precision\t= 0.5506999109717395\n",
      "\t\t召回率 Recall   \t= 0.20296135789093536\n",
      "TP 2636 P 5538 FP 1310 N 3871\n",
      "测试特征47_313\n",
      "\t\t精确率 Precision\t= 0.5844613264098844\n",
      "\t\t召回率 Recall   \t= 0.4759841097869267\n",
      "TP 1906 P 5538 FP 966 N 3871\n",
      "测试特征47_323\n",
      "\t\t精确率 Precision\t= 0.5796843359207859\n",
      "\t\t召回率 Recall   \t= 0.3441675695196822\n",
      "TP 2730 P 5538 FP 1366 N 3871\n",
      "测试特征47_3123\n",
      "\t\t精确率 Precision\t= 0.5828038766125667\n",
      "\t\t召回率 Recall   \t= 0.49295774647887325\n",
      "TP 4 P 5538 FP 3 N 3871\n",
      "测试特征48_31\n",
      "\t\t精确率 Precision\t= 0.48239765717490185\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 1064 P 5538 FP 563 N 3871\n",
      "测试特征48_32\n",
      "\t\t精确率 Precision\t= 0.5691515866898413\n",
      "\t\t召回率 Recall   \t= 0.19212712170458648\n",
      "TP 4 P 5538 FP 3 N 3871\n",
      "测试特征48_3all\n",
      "\t\t精确率 Precision\t= 0.48239765717490185\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 1064 P 5538 FP 563 N 3871\n",
      "测试特征48_312\n",
      "\t\t精确率 Precision\t= 0.5691515866898413\n",
      "\t\t召回率 Recall   \t= 0.19212712170458648\n",
      "TP 4 P 5538 FP 3 N 3871\n",
      "测试特征48_313\n",
      "\t\t精确率 Precision\t= 0.48239765717490185\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 1064 P 5538 FP 563 N 3871\n",
      "测试特征48_323\n",
      "\t\t精确率 Precision\t= 0.5691515866898413\n",
      "\t\t召回率 Recall   \t= 0.19212712170458648\n",
      "TP 1064 P 5538 FP 563 N 3871\n",
      "测试特征48_3123\n",
      "\t\t精确率 Precision\t= 0.5691515866898413\n",
      "\t\t召回率 Recall   \t= 0.19212712170458648\n",
      "TP 1152 P 5538 FP 620 N 3871\n",
      "测试特征49_31\n",
      "\t\t精确率 Precision\t= 0.5649840515943845\n",
      "\t\t召回率 Recall   \t= 0.20801733477789816\n",
      "TP 1152 P 5538 FP 620 N 3871\n",
      "测试特征49_3all\n",
      "\t\t精确率 Precision\t= 0.5649840515943845\n",
      "\t\t召回率 Recall   \t= 0.20801733477789816\n",
      "TP 1152 P 5538 FP 620 N 3871\n",
      "测试特征49_312\n",
      "\t\t精确率 Precision\t= 0.5649840515943845\n",
      "\t\t召回率 Recall   \t= 0.20801733477789816\n",
      "TP 1152 P 5538 FP 620 N 3871\n",
      "测试特征49_313\n",
      "\t\t精确率 Precision\t= 0.5649840515943845\n",
      "\t\t召回率 Recall   \t= 0.20801733477789816\n",
      "TP 1152 P 5538 FP 620 N 3871\n",
      "测试特征49_3123\n",
      "\t\t精确率 Precision\t= 0.5649840515943845\n",
      "\t\t召回率 Recall   \t= 0.20801733477789816\n",
      "TP 1204 P 5538 FP 652 N 3871\n",
      "测试特征50_31\n",
      "\t\t精确率 Precision\t= 0.5634657001303276\n",
      "\t\t召回率 Recall   \t= 0.2174070061394005\n",
      "TP 1204 P 5538 FP 652 N 3871\n",
      "测试特征50_3all\n",
      "\t\t精确率 Precision\t= 0.5634657001303276\n",
      "\t\t召回率 Recall   \t= 0.2174070061394005\n",
      "TP 1204 P 5538 FP 652 N 3871\n",
      "测试特征50_312\n",
      "\t\t精确率 Precision\t= 0.5634657001303276\n",
      "\t\t召回率 Recall   \t= 0.2174070061394005\n",
      "TP 1204 P 5538 FP 652 N 3871\n",
      "测试特征50_313\n",
      "\t\t精确率 Precision\t= 0.5634657001303276\n",
      "\t\t召回率 Recall   \t= 0.2174070061394005\n",
      "TP 1204 P 5538 FP 652 N 3871\n",
      "测试特征50_3123\n",
      "\t\t精确率 Precision\t= 0.5634657001303276\n",
      "\t\t召回率 Recall   \t= 0.2174070061394005\n",
      "TP 5 P 5538 FP 9 N 3871\n",
      "测试特征51_31\n",
      "\t\t精确率 Precision\t= 0.27970865788979293\n",
      "\t\t召回率 Recall   \t= 0.0009028530155290719\n",
      "TP 1340 P 5538 FP 857 N 3871\n",
      "测试特征51_32\n",
      "\t\t精确率 Precision\t= 0.5222019960121637\n",
      "\t\t召回率 Recall   \t= 0.24196460816179127\n",
      "TP 2646 P 5538 FP 1430 N 3871\n",
      "测试特征51_33\n",
      "\t\t精确率 Precision\t= 0.563961161558916\n",
      "\t\t召回率 Recall   \t= 0.47778981581798485\n",
      "TP 5 P 5538 FP 9 N 3871\n",
      "测试特征51_3all\n",
      "\t\t精确率 Precision\t= 0.27970865788979293\n",
      "\t\t召回率 Recall   \t= 0.0009028530155290719\n",
      "TP 1342 P 5538 FP 860 N 3871\n",
      "测试特征51_312\n",
      "\t\t精确率 Precision\t= 0.5217021997954921\n",
      "\t\t召回率 Recall   \t= 0.24232574936800289\n",
      "TP 2650 P 5538 FP 1437 N 3871\n",
      "测试特征51_313\n",
      "\t\t精确率 Precision\t= 0.5631316336353639\n",
      "\t\t召回率 Recall   \t= 0.4785120982304081\n",
      "TP 3986 P 5538 FP 2287 N 3871\n",
      "测试特征51_323\n",
      "\t\t精确率 Precision\t= 0.5491969948473783\n",
      "\t\t召回率 Recall   \t= 0.7197544239797761\n",
      "TP 3987 P 5538 FP 2288 N 3871\n",
      "测试特征51_3123\n",
      "\t\t精确率 Precision\t= 0.549150867396504\n",
      "\t\t召回率 Recall   \t= 0.7199349945828819\n",
      "TP 4 P 5538 FP 2 N 3871\n",
      "测试特征52_31\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 469 P 5538 FP 415 N 3871\n",
      "测试特征52_32\n",
      "\t\t精确率 Precision\t= 0.4413225438764306\n",
      "\t\t召回率 Recall   \t= 0.08468761285662695\n",
      "TP 2340 P 5538 FP 1213 N 3871\n",
      "测试特征52_33\n",
      "\t\t精确率 Precision\t= 0.5741818415548843\n",
      "\t\t召回率 Recall   \t= 0.4225352112676056\n",
      "TP 4 P 5538 FP 2 N 3871\n",
      "测试特征52_3all\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 470 P 5538 FP 415 N 3871\n",
      "测试特征52_312\n",
      "\t\t精确率 Precision\t= 0.4418477574532985\n",
      "\t\t召回率 Recall   \t= 0.08486818345973275\n",
      "TP 2342 P 5538 FP 1213 N 3871\n",
      "测试特征52_313\n",
      "\t\t精确率 Precision\t= 0.574390710892835\n",
      "\t\t召回率 Recall   \t= 0.42289635247381724\n",
      "TP 2737 P 5538 FP 1534 N 3871\n",
      "测试特征52_323\n",
      "\t\t精确率 Precision\t= 0.5549924283215399\n",
      "\t\t召回率 Recall   \t= 0.4942217407006139\n",
      "TP 2737 P 5538 FP 1534 N 3871\n",
      "测试特征52_3123\n",
      "\t\t精确率 Precision\t= 0.5549924283215399\n",
      "\t\t召回率 Recall   \t= 0.4942217407006139\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征53_31\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 4 P 5538 FP 3 N 3871\n",
      "测试特征53_32\n",
      "\t\t精确率 Precision\t= 0.48239765717490185\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 1199 P 5538 FP 711 N 3871\n",
      "测试特征53_33\n",
      "\t\t精确率 Precision\t= 0.5410201394196679\n",
      "\t\t召回率 Recall   \t= 0.21650415312387145\n",
      "TP 2 P 5538 FP 2 N 3871\n",
      "测试特征53_3all\n",
      "\t\t精确率 Precision\t= 0.4114146030396429\n",
      "\t\t召回率 Recall   \t= 0.00036114120621162876\n",
      "TP 5 P 5538 FP 4 N 3871\n",
      "测试特征53_312\n",
      "\t\t精确率 Precision\t= 0.4663068879947961\n",
      "\t\t召回率 Recall   \t= 0.0009028530155290719\n",
      "TP 1199 P 5538 FP 711 N 3871\n",
      "测试特征53_313\n",
      "\t\t精确率 Precision\t= 0.5410201394196679\n",
      "\t\t召回率 Recall   \t= 0.21650415312387145\n",
      "TP 1199 P 5538 FP 711 N 3871\n",
      "测试特征53_323\n",
      "\t\t精确率 Precision\t= 0.5410201394196679\n",
      "\t\t召回率 Recall   \t= 0.21650415312387145\n",
      "TP 1199 P 5538 FP 711 N 3871\n",
      "测试特征53_3123\n",
      "\t\t精确率 Precision\t= 0.5410201394196679\n",
      "\t\t召回率 Recall   \t= 0.21650415312387145\n",
      "TP 4 P 5538 FP 2 N 3871\n",
      "测试特征54_32\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 1201 P 5538 FP 738 N 3871\n",
      "测试特征54_33\n",
      "\t\t精确率 Precision\t= 0.5321668728032998\n",
      "\t\t召回率 Recall   \t= 0.21686529433008306\n",
      "TP 4 P 5538 FP 2 N 3871\n",
      "测试特征54_312\n",
      "\t\t精确率 Precision\t= 0.5829819277108435\n",
      "\t\t召回率 Recall   \t= 0.0007222824124232575\n",
      "TP 1201 P 5538 FP 738 N 3871\n",
      "测试特征54_313\n",
      "\t\t精确率 Precision\t= 0.5321668728032998\n",
      "\t\t召回率 Recall   \t= 0.21686529433008306\n",
      "TP 1201 P 5538 FP 738 N 3871\n",
      "测试特征54_323\n",
      "\t\t精确率 Precision\t= 0.5321668728032998\n",
      "\t\t召回率 Recall   \t= 0.21686529433008306\n",
      "TP 1201 P 5538 FP 738 N 3871\n",
      "测试特征54_3123\n",
      "\t\t精确率 Precision\t= 0.5321668728032998\n",
      "\t\t召回率 Recall   \t= 0.21686529433008306\n",
      "TP 3 P 5538 FP 2 N 3871\n",
      "测试特征55_31\n",
      "\t\t精确率 Precision\t= 0.5118339283353167\n",
      "\t\t召回率 Recall   \t= 0.0005417118093174431\n",
      "TP 1080 P 5538 FP 695 N 3871\n",
      "测试特征55_32\n",
      "\t\t精确率 Precision\t= 0.5206592117405745\n",
      "\t\t召回率 Recall   \t= 0.19501625135427952\n",
      "TP 3 P 5538 FP 2 N 3871\n",
      "测试特征55_3all\n",
      "\t\t精确率 Precision\t= 0.5118339283353167\n",
      "\t\t召回率 Recall   \t= 0.0005417118093174431\n",
      "TP 1080 P 5538 FP 695 N 3871\n",
      "测试特征55_312\n",
      "\t\t精确率 Precision\t= 0.5206592117405745\n",
      "\t\t召回率 Recall   \t= 0.19501625135427952\n",
      "TP 3 P 5538 FP 2 N 3871\n",
      "测试特征55_313\n",
      "\t\t精确率 Precision\t= 0.5118339283353167\n",
      "\t\t召回率 Recall   \t= 0.0005417118093174431\n",
      "TP 1080 P 5538 FP 695 N 3871\n",
      "测试特征55_323\n",
      "\t\t精确率 Precision\t= 0.5206592117405745\n",
      "\t\t召回率 Recall   \t= 0.19501625135427952\n",
      "TP 1080 P 5538 FP 695 N 3871\n",
      "测试特征55_3123\n",
      "\t\t精确率 Precision\t= 0.5206592117405745\n",
      "\t\t召回率 Recall   \t= 0.19501625135427952\n",
      "TP 3302 P 5538 FP 1697 N 3871\n",
      "测试特征56_31\n",
      "\t\t精确率 Precision\t= 0.5762861074837236\n",
      "\t\t召回率 Recall   \t= 0.596244131455399\n",
      "TP 2221 P 5538 FP 2019 N 3871\n",
      "测试特征56_32\n",
      "\t\t精确率 Precision\t= 0.43468404642910785\n",
      "\t\t召回率 Recall   \t= 0.4010473094980137\n",
      "TP 17 P 5538 FP 225 N 3871\n",
      "测试特征56_33\n",
      "\t\t精确率 Precision\t= 0.050163241877735146\n",
      "\t\t召回率 Recall   \t= 0.0030697002527988442\n",
      "TP 3302 P 5538 FP 1697 N 3871\n",
      "测试特征56_3all\n",
      "\t\t精确率 Precision\t= 0.5762861074837236\n",
      "\t\t召回率 Recall   \t= 0.596244131455399\n",
      "TP 5523 P 5538 FP 3716 N 3871\n",
      "测试特征56_312\n",
      "\t\t精确率 Precision\t= 0.5095370473580225\n",
      "\t\t召回率 Recall   \t= 0.9972914409534128\n",
      "TP 3307 P 5538 FP 1788 N 3871\n",
      "测试特征56_313\n",
      "\t\t精确率 Precision\t= 0.5638552052757345\n",
      "\t\t召回率 Recall   \t= 0.5971469844709282\n",
      "TP 2234 P 5538 FP 2173 N 3871\n",
      "测试特征56_323\n",
      "\t\t精确率 Precision\t= 0.41813464998940136\n",
      "\t\t召回率 Recall   \t= 0.4033947273383893\n",
      "TP 5524 P 5538 FP 3736 N 3871\n",
      "测试特征56_3123\n",
      "\t\t精确率 Precision\t= 0.5082407942011399\n",
      "\t\t召回率 Recall   \t= 0.9974720115565185\n",
      "TP 3244 P 5538 FP 1666 N 3871\n",
      "测试特征57_31\n",
      "\t\t精确率 Precision\t= 0.5764607439131921\n",
      "\t\t召回率 Recall   \t= 0.5857710364752619\n",
      "TP 2431 P 5538 FP 2186 N 3871\n",
      "测试特征57_32\n",
      "\t\t精确率 Precision\t= 0.437358053498462\n",
      "\t\t召回率 Recall   \t= 0.43896713615023475\n",
      "TP 10 P 5538 FP 135 N 3871\n",
      "测试特征57_33\n",
      "\t\t精确率 Precision\t= 0.049228069283007346\n",
      "\t\t召回率 Recall   \t= 0.0018057060310581437\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 3244 P 5538 FP 1666 N 3871\n",
      "测试特征57_3all\n",
      "\t\t精确率 Precision\t= 0.5764607439131921\n",
      "\t\t召回率 Recall   \t= 0.5857710364752619\n",
      "TP 5503 P 5538 FP 3692 N 3871\n",
      "测试特征57_312\n",
      "\t\t精确率 Precision\t= 0.5102496959824265\n",
      "\t\t召回率 Recall   \t= 0.9936800288912965\n",
      "TP 3247 P 5538 FP 1703 N 3871\n",
      "测试特征57_313\n",
      "\t\t精确率 Precision\t= 0.5713152940150819\n",
      "\t\t召回率 Recall   \t= 0.5863127482845792\n",
      "TP 2438 P 5538 FP 2278 N 3871\n",
      "测试特征57_323\n",
      "\t\t精确率 Precision\t= 0.42794501734044915\n",
      "\t\t召回率 Recall   \t= 0.4402311303719754\n",
      "TP 5503 P 5538 FP 3703 N 3871\n",
      "测试特征57_3123\n",
      "\t\t精确率 Precision\t= 0.5095062400789183\n",
      "\t\t召回率 Recall   \t= 0.9936800288912965\n",
      "TP 1847 P 5538 FP 981 N 3871\n",
      "测试特征58_31\n",
      "\t\t精确率 Precision\t= 0.568227973501323\n",
      "\t\t召回率 Recall   \t= 0.33351390393643915\n",
      "TP 3538 P 5538 FP 2114 N 3871\n",
      "测试特征58_32\n",
      "\t\t精确率 Precision\t= 0.5391345801448887\n",
      "\t\t召回率 Recall   \t= 0.6388587937883713\n",
      "TP 485 P 5538 FP 1019 N 3871\n",
      "测试特征58_33\n",
      "\t\t精确率 Precision\t= 0.24963709952468247\n",
      "\t\t召回率 Recall   \t= 0.08757674250631997\n",
      "TP 1847 P 5538 FP 981 N 3871\n",
      "测试特征58_3all\n",
      "\t\t精确率 Precision\t= 0.568227973501323\n",
      "\t\t召回率 Recall   \t= 0.33351390393643915\n",
      "TP 5011 P 5538 FP 2812 N 3871\n",
      "测试特征58_312\n",
      "\t\t精确率 Precision\t= 0.5546851187475867\n",
      "\t\t召回率 Recall   \t= 0.9048392921632358\n",
      "TP 2292 P 5538 FP 1882 N 3871\n",
      "测试特征58_313\n",
      "\t\t精确率 Precision\t= 0.4598290693971779\n",
      "\t\t召回率 Recall   \t= 0.41386782231852653\n",
      "TP 4002 P 5538 FP 2955 N 3871\n",
      "测试特征58_323\n",
      "\t\t精确率 Precision\t= 0.4862971901649558\n",
      "\t\t召回率 Recall   \t= 0.7226435536294691\n",
      "TP 5437 P 5538 FP 3561 N 3871\n",
      "测试特征58_3123\n",
      "\t\t精确率 Precision\t= 0.5162606339445605\n",
      "\t\t召回率 Recall   \t= 0.9817623690863128\n",
      "TP 1859 P 5538 FP 987 N 3871\n",
      "测试特征59_31\n",
      "\t\t精确率 Precision\t= 0.5683208164145316\n",
      "\t\t召回率 Recall   \t= 0.33568075117370894\n",
      "TP 3387 P 5538 FP 1997 N 3871\n",
      "测试特征59_32\n",
      "\t\t精确率 Precision\t= 0.5424421120935913\n",
      "\t\t召回率 Recall   \t= 0.6115926327193932\n",
      "TP 433 P 5538 FP 861 N 3871\n",
      "测试特征59_33\n",
      "\t\t精确率 Precision\t= 0.26009452294804714\n",
      "\t\t召回率 Recall   \t= 0.07818707114481763\n",
      "TP 1859 P 5538 FP 987 N 3871\n",
      "测试特征59_3all\n",
      "\t\t精确率 Precision\t= 0.5683208164145316\n",
      "\t\t召回率 Recall   \t= 0.33568075117370894\n",
      "TP 5012 P 5538 FP 2816 N 3871\n",
      "测试特征59_312\n",
      "\t\t精确率 Precision\t= 0.554383271908073\n",
      "\t\t召回率 Recall   \t= 0.9050198627663416\n",
      "TP 2283 P 5538 FP 1775 N 3871\n",
      "测试特征59_313\n",
      "\t\t精确率 Precision\t= 0.4734174359069959\n",
      "\t\t召回率 Recall   \t= 0.4122426868905742\n",
      "TP 3811 P 5538 FP 2760 N 3871\n",
      "测试特征59_323\n",
      "\t\t精确率 Precision\t= 0.4911360260178183\n",
      "\t\t召回率 Recall   \t= 0.6881545684362586\n",
      "TP 5429 P 5538 FP 3516 N 3871\n",
      "测试特征59_3123\n",
      "\t\t精确率 Precision\t= 0.5190683530207164\n",
      "\t\t召回率 Recall   \t= 0.9803178042614662\n",
      "TP 3302 P 5538 FP 1701 N 3871\n",
      "测试特征60_31\n",
      "\t\t精确率 Precision\t= 0.5757111238626116\n",
      "\t\t召回率 Recall   \t= 0.596244131455399\n",
      "TP 2221 P 5538 FP 2019 N 3871\n",
      "测试特征60_32\n",
      "\t\t精确率 Precision\t= 0.43468404642910785\n",
      "\t\t召回率 Recall   \t= 0.4010473094980137\n",
      "TP 106 P 5538 FP 523 N 3871\n",
      "测试特征60_33\n",
      "\t\t精确率 Precision\t= 0.12408927329361599\n",
      "\t\t召回率 Recall   \t= 0.019140483929216325\n",
      "TP 3302 P 5538 FP 1701 N 3871\n",
      "测试特征60_3all\n",
      "\t\t精确率 Precision\t= 0.5757111238626116\n",
      "\t\t召回率 Recall   \t= 0.596244131455399\n",
      "TP 5523 P 5538 FP 3720 N 3871\n",
      "测试特征60_312\n",
      "\t\t精确率 Precision\t= 0.5092681806458953\n",
      "\t\t召回率 Recall   \t= 0.9972914409534128\n",
      "TP 3332 P 5538 FP 1916 N 3871\n",
      "测试特征60_313\n",
      "\t\t精确率 Precision\t= 0.5486487291239347\n",
      "\t\t召回率 Recall   \t= 0.6016612495485735\n",
      "TP 2303 P 5538 FP 2371 N 3871\n",
      "测试特征60_323\n",
      "\t\t精确率 Precision\t= 0.40438677062191936\n",
      "\t\t召回率 Recall   \t= 0.4158540989526905\n",
      "TP 5529 P 5538 FP 3764 N 3871\n",
      "测试特征60_3123\n",
      "\t\t精确率 Precision\t= 0.5066006646359332\n",
      "\t\t召回率 Recall   \t= 0.9983748645720477\n",
      "TP 3248 P 5538 FP 1670 N 3871\n",
      "测试特征61_31\n",
      "\t\t精确率 Precision\t= 0.5761760849453391\n",
      "\t\t召回率 Recall   \t= 0.5864933188876851\n",
      "TP 2426 P 5538 FP 2183 N 3871\n",
      "测试特征61_32\n",
      "\t\t精确率 Precision\t= 0.43718935778962315\n",
      "\t\t召回率 Recall   \t= 0.4380642831347057\n",
      "TP 76 P 5538 FP 345 N 3871\n",
      "测试特征61_33\n",
      "\t\t精确率 Precision\t= 0.13343396199030663\n",
      "\t\t召回率 Recall   \t= 0.013723365836041893\n",
      "TP 3248 P 5538 FP 1670 N 3871\n",
      "测试特征61_3all\n",
      "\t\t精确率 Precision\t= 0.5761760849453391\n",
      "\t\t召回率 Recall   \t= 0.5864933188876851\n",
      "TP 5508 P 5538 FP 3700 N 3871\n",
      "测试特征61_312\n",
      "\t\t精确率 Precision\t= 0.5099357439101074\n",
      "\t\t召回率 Recall   \t= 0.9945828819068255\n",
      "TP 3263 P 5538 FP 1778 N 3871\n",
      "测试特征61_313\n",
      "\t\t精确率 Precision\t= 0.5619395401749747\n",
      "\t\t召回率 Recall   \t= 0.5892018779342723\n",
      "TP 2485 P 5538 FP 2420 N 3871\n",
      "测试特征61_323\n",
      "\t\t精确率 Precision\t= 0.4178476152292248\n",
      "\t\t召回率 Recall   \t= 0.44871794871794873\n",
      "TP 5511 P 5538 FP 3728 N 3871\n",
      "测试特征61_3123\n",
      "\t\t精确率 Precision\t= 0.508187679264828\n",
      "\t\t召回率 Recall   \t= 0.995124593716143\n",
      "TP 1847 P 5538 FP 983 N 3871\n",
      "测试特征62_31\n",
      "\t\t精确率 Precision\t= 0.5677282198540512\n",
      "\t\t召回率 Recall   \t= 0.33351390393643915\n",
      "TP 3538 P 5538 FP 2119 N 3871\n",
      "测试特征62_32\n",
      "\t\t精确率 Precision\t= 0.5385475462257704\n",
      "\t\t召回率 Recall   \t= 0.6388587937883713\n",
      "TP 92 P 5538 FP 411 N 3871\n",
      "测试特征62_33\n",
      "\t\t精确率 Precision\t= 0.13529565960680026\n",
      "\t\t召回率 Recall   \t= 0.016612495485734922\n",
      "TP 1847 P 5538 FP 983 N 3871\n",
      "测试特征62_3all\n",
      "\t\t精确率 Precision\t= 0.5677282198540512\n",
      "\t\t召回率 Recall   \t= 0.33351390393643915\n",
      "TP 5011 P 5538 FP 2818 N 3871\n",
      "测试特征62_312\n",
      "\t\t精确率 Precision\t= 0.5541585716809939\n",
      "\t\t召回率 Recall   \t= 0.9048392921632358\n",
      "TP 1897 P 5538 FP 1231 N 3871\n",
      "测试特征62_313\n",
      "\t\t精确率 Precision\t= 0.518573023039688\n",
      "\t\t召回率 Recall   \t= 0.34254243409172985\n",
      "TP 3581 P 5538 FP 2322 N 3871\n",
      "测试特征62_323\n",
      "\t\t精确率 Precision\t= 0.518764346941822\n",
      "\t\t召回率 Recall   \t= 0.6466233297219213\n",
      "TP 5020 P 5538 FP 2897 N 3871\n",
      "测试特征62_3123\n",
      "\t\t精确率 Precision\t= 0.547762338297045\n",
      "\t\t召回率 Recall   \t= 0.9064644275911882\n",
      "TP 1859 P 5538 FP 989 N 3871\n",
      "测试特征63_31\n",
      "\t\t精确率 Precision\t= 0.5678241237009767\n",
      "\t\t召回率 Recall   \t= 0.33568075117370894\n",
      "TP 3388 P 5538 FP 2003 N 3871\n",
      "测试特征63_32\n",
      "\t\t精确率 Precision\t= 0.5417707078474073\n",
      "\t\t召回率 Recall   \t= 0.6117732033224991\n",
      "TP 95 P 5538 FP 430 N 3871\n",
      "测试特征63_33\n",
      "\t\t精确率 Precision\t= 0.1337699634605696\n",
      "\t\t召回率 Recall   \t= 0.017154207295052366\n",
      "TP 1859 P 5538 FP 989 N 3871\n",
      "测试特征63_3all\n",
      "\t\t精确率 Precision\t= 0.5678241237009767\n",
      "\t\t召回率 Recall   \t= 0.33568075117370894\n",
      "TP 5013 P 5538 FP 2823 N 3871\n",
      "测试特征63_312\n",
      "\t\t精确率 Precision\t= 0.5538191523600051\n",
      "\t\t召回率 Recall   \t= 0.9052004333694474\n",
      "TP 1913 P 5538 FP 1252 N 3871\n",
      "测试特征63_313\n",
      "\t\t精确率 Precision\t= 0.5164465308426458\n",
      "\t\t召回率 Recall   \t= 0.3454315637414229\n",
      "TP 3433 P 5538 FP 2232 N 3871\n",
      "测试特征63_323\n",
      "\t\t精确率 Precision\t= 0.5180960718104851\n",
      "\t\t召回率 Recall   \t= 0.6198988804622607\n",
      "TP 5022 P 5538 FP 2912 N 3871\n",
      "测试特征63_3123\n",
      "\t\t精确率 Precision\t= 0.5465814231680776\n",
      "\t\t召回率 Recall   \t= 0.9068255687973997\n",
      "TP 1831 P 4322 FP 1104 N 2784\n",
      "测试特征64_31\n",
      "\t\t精确率 Precision\t= 0.5165171883815489\n",
      "\t\t召回率 Recall   \t= 0.4236464599722351\n",
      "TP 2071 P 4322 FP 1368 N 2784\n",
      "测试特征64_32\n",
      "\t\t精确率 Precision\t= 0.49371339320577906\n",
      "\t\t召回率 Recall   \t= 0.47917630726515503\n",
      "TP 680 P 4322 FP 478 N 2784\n",
      "测试特征64_33\n",
      "\t\t精确率 Precision\t= 0.4781770107672676\n",
      "\t\t召回率 Recall   \t= 0.15733456732993983\n",
      "TP 1831 P 4322 FP 1104 N 2784\n",
      "测试特征64_3all\n",
      "\t\t精确率 Precision\t= 0.5165171883815489\n",
      "\t\t召回率 Recall   \t= 0.4236464599722351\n",
      "TP 3804 P 4322 FP 2406 N 2784\n",
      "测试特征64_312\n",
      "\t\t精确率 Precision\t= 0.5045643760837785\n",
      "\t\t召回率 Recall   \t= 0.8801480795927811\n",
      "TP 2464 P 4322 FP 1532 N 2784\n",
      "测试特征64_313\n",
      "\t\t精确率 Precision\t= 0.5088446919682993\n",
      "\t\t召回率 Recall   \t= 0.5701064322073114\n",
      "TP 2548 P 4322 FP 1724 N 2784\n",
      "测试特征64_323\n",
      "\t\t精确率 Precision\t= 0.4877104881758104\n",
      "\t\t召回率 Recall   \t= 0.5895418787598334\n",
      "TP 4238 P 4322 FP 2720 N 2784\n",
      "测试特征64_3123\n",
      "\t\t精确率 Precision\t= 0.5009075149848657\n",
      "\t\t召回率 Recall   \t= 0.9805645534474781\n",
      "TP 2750 P 4322 FP 1606 N 2784\n",
      "测试特征65_31\n",
      "\t\t精确率 Precision\t= 0.5244865909275876\n",
      "\t\t召回率 Recall   \t= 0.6362795002313744\n",
      "TP 1329 P 4322 FP 959 N 2784\n",
      "测试特征65_32\n",
      "\t\t精确率 Precision\t= 0.47164581998573823\n",
      "\t\t召回率 Recall   \t= 0.3074965293845442\n",
      "TP 307 P 4322 FP 252 N 2784\n",
      "测试特征65_33\n",
      "\t\t精确率 Precision\t= 0.439692319089304\n",
      "\t\t召回率 Recall   \t= 0.07103192966219343\n",
      "TP 2750 P 4322 FP 1606 N 2784\n",
      "测试特征65_3all\n",
      "\t\t精确率 Precision\t= 0.5244865909275876\n",
      "\t\t召回率 Recall   \t= 0.6362795002313744\n",
      "TP 3895 P 4322 FP 2440 N 2784\n",
      "测试特征65_312\n",
      "\t\t精确率 Precision\t= 0.5069660803315293\n",
      "\t\t召回率 Recall   \t= 0.9012031466913466\n",
      "TP 2997 P 4322 FP 1790 N 2784\n",
      "测试特征65_313\n",
      "\t\t精确率 Precision\t= 0.5188826785625\n",
      "\t\t召回率 Recall   \t= 0.6934289680703378\n",
      "TP 1613 P 4322 FP 1175 N 2784\n",
      "测试特征65_323\n",
      "\t\t精确率 Precision\t= 0.46928824524174145\n",
      "\t\t召回率 Recall   \t= 0.3732068486811661\n",
      "TP 4128 P 4322 FP 2611 N 2784\n",
      "测试特征65_3123\n",
      "\t\t精确率 Precision\t= 0.5045574294947371\n",
      "\t\t召回率 Recall   \t= 0.955113373438223\n",
      "TP 1238 P 4322 FP 818 N 2784\n",
      "测试特征66_31\n",
      "\t\t精确率 Precision\t= 0.4936404932234201\n",
      "\t\t召回率 Recall   \t= 0.2864414622859787\n",
      "TP 3466 P 4322 FP 2254 N 2784\n",
      "测试特征66_32\n",
      "\t\t精确率 Precision\t= 0.4976163330743146\n",
      "\t\t召回率 Recall   \t= 0.8019435446552522\n",
      "TP 66 P 4322 FP 71 N 2784\n",
      "测试特征66_33\n",
      "\t\t精确率 Precision\t= 0.3745245675756106\n",
      "\t\t召回率 Recall   \t= 0.015270708005552984\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 1238 P 4322 FP 818 N 2784\n",
      "测试特征66_3all\n",
      "\t\t精确率 Precision\t= 0.4936404932234201\n",
      "\t\t召回率 Recall   \t= 0.2864414622859787\n",
      "TP 4262 P 4322 FP 2733 N 2784\n",
      "测试特征66_312\n",
      "\t\t精确率 Precision\t= 0.5011272758695051\n",
      "\t\t召回率 Recall   \t= 0.9861175381767701\n",
      "TP 1281 P 4322 FP 850 N 2784\n",
      "测试特征66_313\n",
      "\t\t精确率 Precision\t= 0.4925831532689761\n",
      "\t\t召回率 Recall   \t= 0.2963905599259602\n",
      "TP 3482 P 4322 FP 2267 N 2784\n",
      "测试特征66_323\n",
      "\t\t精确率 Precision\t= 0.497330013930942\n",
      "\t\t召回率 Recall   \t= 0.8056455344747802\n",
      "TP 4278 P 4322 FP 2746 N 2784\n",
      "测试特征66_3123\n",
      "\t\t精确率 Precision\t= 0.5008776960239656\n",
      "\t\t召回率 Recall   \t= 0.989819527996298\n",
      "TP 1242 P 4322 FP 821 N 2784\n",
      "测试特征67_31\n",
      "\t\t精确率 Precision\t= 0.4935317702170541\n",
      "\t\t召回率 Recall   \t= 0.28736695974086074\n",
      "TP 2737 P 4322 FP 1709 N 2784\n",
      "测试特征67_32\n",
      "\t\t精确率 Precision\t= 0.5077804994846764\n",
      "\t\t召回率 Recall   \t= 0.6332716335030079\n",
      "TP 827 P 4322 FP 555 N 2784\n",
      "测试特征67_33\n",
      "\t\t精确率 Precision\t= 0.48975320128702393\n",
      "\t\t召回率 Recall   \t= 0.1913465987968533\n",
      "TP 1242 P 4322 FP 821 N 2784\n",
      "测试特征67_3all\n",
      "\t\t精确率 Precision\t= 0.4935317702170541\n",
      "\t\t召回率 Recall   \t= 0.28736695974086074\n",
      "TP 3864 P 4322 FP 2434 N 2784\n",
      "测试特征67_312\n",
      "\t\t精确率 Precision\t= 0.5055841193574846\n",
      "\t\t召回率 Recall   \t= 0.8940305414160111\n",
      "TP 1930 P 4322 FP 1259 N 2784\n",
      "测试特征67_313\n",
      "\t\t精确率 Precision\t= 0.4968432250054972\n",
      "\t\t召回率 Recall   \t= 0.44655252198056455\n",
      "TP 3220 P 4322 FP 2057 N 2784\n",
      "测试特征67_323\n",
      "\t\t精确率 Precision\t= 0.5020757963921704\n",
      "\t\t召回率 Recall   \t= 0.7450254511800093\n",
      "TP 4222 P 4322 FP 2693 N 2784\n",
      "测试特征67_3123\n",
      "\t\t精确率 Precision\t= 0.5024558852459394\n",
      "\t\t召回率 Recall   \t= 0.97686256362795\n",
      "TP 2379 P 4322 FP 1412 N 2784\n",
      "测试特征68_31\n",
      "\t\t精确率 Precision\t= 0.5204494805827532\n",
      "\t\t召回率 Recall   \t= 0.550439611291069\n",
      "TP 2270 P 4322 FP 1579 N 2784\n",
      "测试特征68_32\n",
      "\t\t精确率 Precision\t= 0.4807990920349315\n",
      "\t\t召回率 Recall   \t= 0.5252198056455345\n",
      "TP 1214 P 4322 FP 742 N 2784\n",
      "测试特征68_33\n",
      "\t\t精确率 Precision\t= 0.5131212898720148\n",
      "\t\t召回率 Recall   \t= 0.2808884775566867\n",
      "TP 2379 P 4322 FP 1412 N 2784\n",
      "测试特征68_3all\n",
      "\t\t精确率 Precision\t= 0.5204494805827532\n",
      "\t\t召回率 Recall   \t= 0.550439611291069\n",
      "TP 3840 P 4322 FP 2459 N 2784\n",
      "测试特征68_312\n",
      "\t\t精确率 Precision\t= 0.5014720176854146\n",
      "\t\t召回率 Recall   \t= 0.8884775566867191\n",
      "TP 3068 P 4322 FP 1851 N 2784\n",
      "测试特征68_313\n",
      "\t\t精确率 Precision\t= 0.5163617396275294\n",
      "\t\t召回率 Recall   \t= 0.7098565478944933\n",
      "TP 2933 P 4322 FP 1974 N 2784\n",
      "测试特征68_323\n",
      "\t\t精确率 Precision\t= 0.48903534146648225\n",
      "\t\t召回率 Recall   \t= 0.6786210087922259\n",
      "TP 4307 P 4322 FP 2775 N 2784\n",
      "测试特征68_3123\n",
      "\t\t精确率 Precision\t= 0.49994033581554687\n",
      "\t\t召回率 Recall   \t= 0.9965293845441925\n",
      "TP 2324 P 4322 FP 1386 N 2784\n",
      "测试特征69_31\n",
      "\t\t精确率 Precision\t= 0.5192500859529314\n",
      "\t\t召回率 Recall   \t= 0.5377140212864414\n",
      "TP 2369 P 4322 FP 1637 N 2784\n",
      "测试特征69_32\n",
      "\t\t精确率 Precision\t= 0.48245048978048216\n",
      "\t\t召回率 Recall   \t= 0.548125867653864\n",
      "TP 637 P 4322 FP 416 N 2784\n",
      "测试特征69_33\n",
      "\t\t精确率 Precision\t= 0.4965637740244612\n",
      "\t\t召回率 Recall   \t= 0.14738546968995836\n",
      "TP 2324 P 4322 FP 1386 N 2784\n",
      "测试特征69_3all\n",
      "\t\t精确率 Precision\t= 0.5192500859529314\n",
      "\t\t召回率 Recall   \t= 0.5377140212864414\n",
      "TP 3865 P 4322 FP 2478 N 2784\n",
      "测试特征69_312\n",
      "\t\t精确率 Precision\t= 0.5011700936689744\n",
      "\t\t召回率 Recall   \t= 0.8942619157797316\n",
      "TP 2697 P 4322 FP 1607 N 2784\n",
      "测试特征69_313\n",
      "\t\t精确率 Precision\t= 0.5194755021861917\n",
      "\t\t召回率 Recall   \t= 0.6240166589541879\n",
      "TP 2691 P 4322 FP 1838 N 2784\n",
      "测试特征69_323\n",
      "\t\t精确率 Precision\t= 0.4853555227597537\n",
      "\t\t召回率 Recall   \t= 0.6226284127718649\n",
      "TP 4104 P 4322 FP 2628 N 2784\n",
      "测试特征69_3123\n",
      "\t\t精确率 Precision\t= 0.5014773686089982\n",
      "\t\t召回率 Recall   \t= 0.949560388708931\n",
      "TP 1463 P 4322 FP 941 N 2784\n",
      "测试特征70_31\n",
      "\t\t精确率 Precision\t= 0.5003679363891422\n",
      "\t\t召回率 Recall   \t= 0.33850069412309114\n",
      "TP 3256 P 4322 FP 2140 N 2784\n",
      "测试特征70_32\n",
      "\t\t精确率 Precision\t= 0.49496619595382363\n",
      "\t\t召回率 Recall   \t= 0.7533549282739472\n",
      "TP 837 P 4322 FP 523 N 2784\n",
      "测试特征70_33\n",
      "\t\t精确率 Precision\t= 0.5076026866994262\n",
      "\t\t召回率 Recall   \t= 0.1936603424340583\n",
      "TP 1463 P 4322 FP 941 N 2784\n",
      "测试特征70_3all\n",
      "\t\t精确率 Precision\t= 0.5003679363891422\n",
      "\t\t召回率 Recall   \t= 0.33850069412309114\n",
      "TP 3997 P 4322 FP 2553 N 2784\n",
      "测试特征70_312\n",
      "\t\t精确率 Precision\t= 0.5021113439150059\n",
      "\t\t召回率 Recall   \t= 0.9248033317908376\n",
      "TP 1981 P 4322 FP 1236 N 2784\n",
      "测试特征70_313\n",
      "\t\t精确率 Precision\t= 0.5079722975646527\n",
      "\t\t召回率 Recall   \t= 0.45835261453031007\n",
      "TP 3574 P 4322 FP 2346 N 2784\n",
      "测试特征70_323\n",
      "\t\t精确率 Precision\t= 0.49528617738643427\n",
      "\t\t召回率 Recall   \t= 0.8269319759370661\n",
      "TP 4213 P 4322 FP 2702 N 2784\n",
      "测试特征70_3123\n",
      "\t\t精确率 Precision\t= 0.5010883052429193\n",
      "\t\t召回率 Recall   \t= 0.9747801943544655\n",
      "TP 1475 P 4322 FP 942 N 2784\n",
      "测试特征71_31\n",
      "\t\t精确率 Precision\t= 0.502144606494423\n",
      "\t\t召回率 Recall   \t= 0.34127718648773714\n",
      "TP 2599 P 4322 FP 1691 N 2784\n",
      "测试特征71_32\n",
      "\t\t精确率 Precision\t= 0.4974943135087326\n",
      "\t\t召回率 Recall   \t= 0.6013419713095789\n",
      "TP 2022 P 4322 FP 1289 N 2784\n",
      "测试特征71_33\n",
      "\t\t精确率 Precision\t= 0.502597696884353\n",
      "\t\t召回率 Recall   \t= 0.46783896344285053\n",
      "TP 1475 P 4322 FP 942 N 2784\n",
      "测试特征71_3all\n",
      "\t\t精确率 Precision\t= 0.502144606494423\n",
      "\t\t召回率 Recall   \t= 0.34127718648773714\n",
      "TP 3602 P 4322 FP 2289 N 2784\n",
      "测试特征71_312\n",
      "\t\t精确率 Precision\t= 0.5033861207751046\n",
      "\t\t召回率 Recall   \t= 0.8334104581212401\n",
      "TP 2821 P 4322 FP 1760 N 2784\n",
      "测试特征71_313\n",
      "\t\t精确率 Precision\t= 0.5079863475577321\n",
      "\t\t召回率 Recall   \t= 0.6527070800555298\n",
      "TP 3690 P 4322 FP 2406 N 2784\n",
      "测试特征71_323\n",
      "\t\t精确率 Precision\t= 0.49695786876081555\n",
      "\t\t召回率 Recall   \t= 0.8537714021286441\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征71_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 93 P 4322 FP 50 N 2784\n",
      "测试特征72_31\n",
      "\t\t精确率 Precision\t= 0.5450641246957971\n",
      "\t\t召回率 Recall   \t= 0.021517815826006478\n",
      "TP 1223 P 4322 FP 803 N 2784\n",
      "测试特征72_32\n",
      "\t\t精确率 Precision\t= 0.49521962219496235\n",
      "\t\t召回率 Recall   \t= 0.2829708468301712\n",
      "TP 370 P 4322 FP 191 N 2784\n",
      "测试特征72_33\n",
      "\t\t精确率 Precision\t= 0.5551250227691367\n",
      "\t\t召回率 Recall   \t= 0.08560851457658492\n",
      "TP 93 P 4322 FP 50 N 2784\n",
      "测试特征72_3all\n",
      "\t\t精确率 Precision\t= 0.5450641246957971\n",
      "\t\t召回率 Recall   \t= 0.021517815826006478\n",
      "TP 1290 P 4322 FP 842 N 2784\n",
      "测试特征72_312\n",
      "\t\t精确率 Precision\t= 0.49669703992153225\n",
      "\t\t召回率 Recall   \t= 0.2984729291994447\n",
      "TP 463 P 4322 FP 241 N 2784\n",
      "测试特征72_313\n",
      "\t\t精确率 Precision\t= 0.5530744522641009\n",
      "\t\t召回率 Recall   \t= 0.1071263304025914\n",
      "TP 1593 P 4322 FP 994 N 2784\n",
      "测试特征72_323\n",
      "\t\t精确率 Precision\t= 0.5079512265518877\n",
      "\t\t召回率 Recall   \t= 0.3685793614067561\n",
      "TP 1660 P 4322 FP 1033 N 2784\n",
      "测试特征72_3123\n",
      "\t\t精确率 Precision\t= 0.5086293672090869\n",
      "\t\t召回率 Recall   \t= 0.3840814437760296\n",
      "TP 59 P 4322 FP 22 N 2784\n",
      "测试特征73_31\n",
      "\t\t精确率 Precision\t= 0.6333616102413819\n",
      "\t\t召回率 Recall   \t= 0.013651087459509487\n",
      "TP 1225 P 4322 FP 809 N 2784\n",
      "测试特征73_32\n",
      "\t\t精确率 Precision\t= 0.4937672454407174\n",
      "\t\t召回率 Recall   \t= 0.2834335955576122\n",
      "TP 193 P 4322 FP 95 N 2784\n",
      "测试特征73_33\n",
      "\t\t精确率 Precision\t= 0.566843407862838\n",
      "\t\t召回率 Recall   \t= 0.04465525219805645\n",
      "TP 59 P 4322 FP 22 N 2784\n",
      "测试特征73_3all\n",
      "\t\t精确率 Precision\t= 0.6333616102413819\n",
      "\t\t召回率 Recall   \t= 0.013651087459509487\n",
      "TP 1275 P 4322 FP 824 N 2784\n",
      "测试特征73_312\n",
      "\t\t精确率 Precision\t= 0.4991753537653595\n",
      "\t\t召回率 Recall   \t= 0.2950023137436372\n",
      "TP 246 P 4322 FP 116 N 2784\n",
      "测试特征73_313\n",
      "\t\t精确率 Precision\t= 0.577351848230002\n",
      "\t\t召回率 Recall   \t= 0.056918093475242945\n",
      "TP 1418 P 4322 FP 904 N 2784\n",
      "测试特征73_323\n",
      "\t\t精确率 Precision\t= 0.5025859347150786\n",
      "\t\t召回率 Recall   \t= 0.32808884775566866\n",
      "TP 1462 P 4322 FP 918 N 2784\n",
      "测试特征73_3123\n",
      "\t\t精确率 Precision\t= 0.5063830867236873\n",
      "\t\t召回率 Recall   \t= 0.33826931975937063\n",
      "TP 130 P 4322 FP 64 N 2784\n",
      "测试特征74_31\n",
      "\t\t精确率 Precision\t= 0.5668036483913\n",
      "\t\t召回率 Recall   \t= 0.03007866728366497\n",
      "TP 1178 P 4322 FP 803 N 2784\n",
      "测试特征74_32\n",
      "\t\t精确率 Precision\t= 0.48585106215920965\n",
      "\t\t召回率 Recall   \t= 0.2725590004627487\n",
      "TP 130 P 4322 FP 64 N 2784\n",
      "测试特征74_3all\n",
      "\t\t精确率 Precision\t= 0.5668036483913\n",
      "\t\t召回率 Recall   \t= 0.03007866728366497\n",
      "TP 1269 P 4322 FP 840 N 2784\n",
      "测试特征74_312\n",
      "\t\t精确率 Precision\t= 0.4931886864517513\n",
      "\t\t召回率 Recall   \t= 0.2936140675613142\n",
      "TP 130 P 4322 FP 65 N 2784\n",
      "测试特征74_313\n",
      "\t\t精确率 Precision\t= 0.5629929221435793\n",
      "\t\t召回率 Recall   \t= 0.03007866728366497\n",
      "TP 1178 P 4322 FP 803 N 2784\n",
      "测试特征74_323\n",
      "\t\t精确率 Precision\t= 0.48585106215920965\n",
      "\t\t召回率 Recall   \t= 0.2725590004627487\n",
      "TP 1269 P 4322 FP 840 N 2784\n",
      "测试特征74_3123\n",
      "\t\t精确率 Precision\t= 0.4931886864517513\n",
      "\t\t召回率 Recall   \t= 0.2936140675613142\n",
      "TP 135 P 4322 FP 62 N 2784\n",
      "测试特征75_31\n",
      "\t\t精确率 Precision\t= 0.583780156693652\n",
      "\t\t召回率 Recall   \t= 0.03123553910226747\n",
      "TP 1180 P 4322 FP 806 N 2784\n",
      "测试特征75_32\n",
      "\t\t精确率 Precision\t= 0.4853433150352538\n",
      "\t\t召回率 Recall   \t= 0.27302174919018973\n",
      "TP 135 P 4322 FP 62 N 2784\n",
      "测试特征75_3all\n",
      "\t\t精确率 Precision\t= 0.583780156693652\n",
      "\t\t召回率 Recall   \t= 0.03123553910226747\n",
      "TP 1283 P 4322 FP 846 N 2784\n",
      "测试特征75_312\n",
      "\t\t精确率 Precision\t= 0.4941521390139071\n",
      "\t\t召回率 Recall   \t= 0.2968533086534012\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 135 P 4322 FP 63 N 2784\n",
      "测试特征75_313\n",
      "\t\t精确率 Precision\t= 0.5798872441469715\n",
      "\t\t召回率 Recall   \t= 0.03123553910226747\n",
      "TP 1180 P 4322 FP 806 N 2784\n",
      "测试特征75_323\n",
      "\t\t精确率 Precision\t= 0.4853433150352538\n",
      "\t\t召回率 Recall   \t= 0.27302174919018973\n",
      "TP 1283 P 4322 FP 846 N 2784\n",
      "测试特征75_3123\n",
      "\t\t精确率 Precision\t= 0.4941521390139071\n",
      "\t\t召回率 Recall   \t= 0.2968533086534012\n",
      "false\n",
      "false\n",
      "false\n",
      "false\n",
      "TP 2739 P 5538 FP 1546 N 3871\n",
      "测试特征80_31\n",
      "\t\t精确率 Precision\t= 0.5532476672783733\n",
      "\t\t召回率 Recall   \t= 0.4945828819068256\n",
      "TP 3074 P 5538 FP 1928 N 3871\n",
      "测试特征80_32\n",
      "\t\t精确率 Precision\t= 0.5270674860712704\n",
      "\t\t召回率 Recall   \t= 0.5550740339472734\n",
      "TP 2980 P 5538 FP 2247 N 3871\n",
      "测试特征80_33\n",
      "\t\t精确率 Precision\t= 0.4810607542303069\n",
      "\t\t召回率 Recall   \t= 0.5381003972553269\n",
      "TP 2739 P 5538 FP 1546 N 3871\n",
      "测试特征80_3all\n",
      "\t\t精确率 Precision\t= 0.5532476672783733\n",
      "\t\t召回率 Recall   \t= 0.4945828819068256\n",
      "TP 4613 P 5538 FP 2758 N 3871\n",
      "测试特征80_312\n",
      "\t\t精确率 Precision\t= 0.5389837355515923\n",
      "\t\t召回率 Recall   \t= 0.8329721921271217\n",
      "TP 4244 P 5538 FP 2963 N 3871\n",
      "测试特征80_313\n",
      "\t\t精确率 Precision\t= 0.5002958497172358\n",
      "\t\t召回率 Recall   \t= 0.7663416395810762\n",
      "TP 4757 P 5538 FP 3379 N 3871\n",
      "测试特征80_323\n",
      "\t\t精确率 Precision\t= 0.49597930080949443\n",
      "\t\t召回率 Recall   \t= 0.8589743589743589\n",
      "TP 5522 P 5538 FP 3844 N 3871\n",
      "测试特征80_3123\n",
      "\t\t精确率 Precision\t= 0.5010265158694267\n",
      "\t\t召回率 Recall   \t= 0.997110870350307\n",
      "TP 2705 P 5538 FP 1521 N 3871\n",
      "测试特征81_31\n",
      "\t\t精确率 Precision\t= 0.554189656560349\n",
      "\t\t召回率 Recall   \t= 0.4884434814012279\n",
      "TP 3025 P 5538 FP 1890 N 3871\n",
      "测试特征81_32\n",
      "\t\t精确率 Precision\t= 0.5280240271331105\n",
      "\t\t召回率 Recall   \t= 0.5462260743950885\n",
      "TP 2407 P 5538 FP 1876 N 3871\n",
      "测试特征81_33\n",
      "\t\t精确率 Precision\t= 0.4728065486074974\n",
      "\t\t召回率 Recall   \t= 0.4346334416756952\n",
      "TP 2705 P 5538 FP 1521 N 3871\n",
      "测试特征81_3all\n",
      "\t\t精确率 Precision\t= 0.554189656560349\n",
      "\t\t召回率 Recall   \t= 0.4884434814012279\n",
      "TP 4559 P 5538 FP 2711 N 3871\n",
      "测试特征81_312\n",
      "\t\t精确率 Precision\t= 0.5403284984017988\n",
      "\t\t召回率 Recall   \t= 0.8232213795594078\n",
      "TP 4010 P 5538 FP 2731 N 3871\n",
      "测试特征81_313\n",
      "\t\t精确率 Precision\t= 0.5065003451562679\n",
      "\t\t召回率 Recall   \t= 0.7240881184543156\n",
      "TP 4384 P 5538 FP 3101 N 3871\n",
      "测试特征81_323\n",
      "\t\t精确率 Precision\t= 0.49702912405595606\n",
      "\t\t召回率 Recall   \t= 0.7916215240158903\n",
      "TP 5389 P 5538 FP 3650 N 3871\n",
      "测试特征81_3123\n",
      "\t\t精确率 Precision\t= 0.5078773776997851\n",
      "\t\t召回率 Recall   \t= 0.9730949801372336\n",
      "TP 1564 P 5538 FP 921 N 3871\n",
      "测试特征82_31\n",
      "\t\t精确率 Precision\t= 0.5427506974163991\n",
      "\t\t召回率 Recall   \t= 0.28241242325749366\n",
      "TP 4021 P 5538 FP 2720 N 3871\n",
      "测试特征82_32\n",
      "\t\t精确率 Precision\t= 0.5081938150002101\n",
      "\t\t召回率 Recall   \t= 0.7260743950884796\n",
      "TP 1092 P 5538 FP 716 N 3871\n",
      "测试特征82_33\n",
      "\t\t精确率 Precision\t= 0.5159859087879654\n",
      "\t\t召回率 Recall   \t= 0.19718309859154928\n",
      "TP 1564 P 5538 FP 921 N 3871\n",
      "测试特征82_3all\n",
      "\t\t精确率 Precision\t= 0.5427506974163991\n",
      "\t\t召回率 Recall   \t= 0.28241242325749366\n",
      "TP 5218 P 5538 FP 3320 N 3871\n",
      "测试特征82_312\n",
      "\t\t精确率 Precision\t= 0.5234899081866915\n",
      "\t\t召回率 Recall   \t= 0.9422174070061394\n",
      "TP 2108 P 5538 FP 1265 N 3871\n",
      "测试特征82_313\n",
      "\t\t精确率 Precision\t= 0.5380629552149405\n",
      "\t\t召回率 Recall   \t= 0.3806428313470567\n",
      "TP 4441 P 5538 FP 3028 N 3871\n",
      "测试特征82_323\n",
      "\t\t精确率 Precision\t= 0.5062138519604212\n",
      "\t\t召回率 Recall   \t= 0.8019140483929217\n",
      "TP 5250 P 5538 FP 3393 N 3871\n",
      "测试特征82_3123\n",
      "\t\t精确率 Precision\t= 0.5195882288693244\n",
      "\t\t召回率 Recall   \t= 0.9479956663055255\n",
      "TP 1571 P 5538 FP 925 N 3871\n",
      "测试特征83_31\n",
      "\t\t精确率 Precision\t= 0.5427834599295912\n",
      "\t\t召回率 Recall   \t= 0.2836764174792344\n",
      "TP 3777 P 5538 FP 2545 N 3871\n",
      "测试特征83_32\n",
      "\t\t精确率 Precision\t= 0.5091686822524705\n",
      "\t\t召回率 Recall   \t= 0.6820151679306609\n",
      "TP 1941 P 5538 FP 1276 N 3871\n",
      "测试特征83_33\n",
      "\t\t精确率 Precision\t= 0.5153333320987739\n",
      "\t\t召回率 Recall   \t= 0.3504875406283857\n",
      "TP 1571 P 5538 FP 925 N 3871\n",
      "测试特征83_3all\n",
      "\t\t精确率 Precision\t= 0.5427834599295912\n",
      "\t\t召回率 Recall   \t= 0.2836764174792344\n",
      "TP 4869 P 5538 FP 3157 N 3871\n",
      "测试特征83_312\n",
      "\t\t精确率 Precision\t= 0.5187776181819759\n",
      "\t\t召回率 Recall   \t= 0.8791982665222102\n",
      "TP 2823 P 5538 FP 1722 N 3871\n",
      "测试特征83_313\n",
      "\t\t精确率 Precision\t= 0.5339957659860707\n",
      "\t\t召回率 Recall   \t= 0.5097508125677139\n",
      "TP 4542 P 5538 FP 3074 N 3871\n",
      "测试特征83_323\n",
      "\t\t精确率 Precision\t= 0.5080661115126806\n",
      "\t\t召回率 Recall   \t= 0.8201516793066089\n",
      "TP 5218 P 5538 FP 3420 N 3871\n",
      "测试特征83_3123\n",
      "\t\t精确率 Precision\t= 0.5160827206980442\n",
      "\t\t召回率 Recall   \t= 0.9422174070061394\n",
      "TP 2 P 4800 FP 2 N 2608\n",
      "测试特征84_31\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 417 P 4800 FP 276 N 2608\n",
      "测试特征84_32\n",
      "\t\t精确率 Precision\t= 0.4508227709572796\n",
      "\t\t召回率 Recall   \t= 0.086875\n",
      "TP 2 P 4800 FP 2 N 2608\n",
      "测试特征84_3all\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 417 P 4800 FP 276 N 2608\n",
      "测试特征84_312\n",
      "\t\t精确率 Precision\t= 0.4508227709572796\n",
      "\t\t召回率 Recall   \t= 0.086875\n",
      "TP 2 P 4800 FP 3 N 2608\n",
      "测试特征84_313\n",
      "\t\t精确率 Precision\t= 0.265905383360522\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 417 P 4800 FP 277 N 2608\n",
      "测试特征84_323\n",
      "\t\t精确率 Precision\t= 0.44992751752487237\n",
      "\t\t召回率 Recall   \t= 0.086875\n",
      "TP 417 P 4800 FP 277 N 2608\n",
      "测试特征84_3123\n",
      "\t\t精确率 Precision\t= 0.44992751752487237\n",
      "\t\t召回率 Recall   \t= 0.086875\n",
      "TP 2 P 4800 FP 4 N 2608\n",
      "测试特征85_32\n",
      "\t\t精确率 Precision\t= 0.21363040629095675\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 3 P 4800 FP 1 N 2608\n",
      "测试特征85_33\n",
      "\t\t精确率 Precision\t= 0.6197718631178707\n",
      "\t\t召回率 Recall   \t= 0.000625\n",
      "TP 2 P 4800 FP 5 N 2608\n",
      "测试特征85_312\n",
      "\t\t精确率 Precision\t= 0.17853231106243156\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 3 P 4800 FP 3 N 2608\n",
      "测试特征85_313\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.000625\n",
      "TP 5 P 4800 FP 5 N 2608\n",
      "测试特征85_323\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.0010416666666666667\n",
      "TP 5 P 4800 FP 6 N 2608\n",
      "测试特征85_3123\n",
      "\t\t精确率 Precision\t= 0.3116634799235181\n",
      "\t\t召回率 Recall   \t= 0.0010416666666666667\n",
      "TP 2 P 4800 FP 1 N 2608\n",
      "测试特征86_32\n",
      "\t\t精确率 Precision\t= 0.5207667731629393\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 414 P 4800 FP 280 N 2608\n",
      "测试特征86_33\n",
      "\t\t精确率 Precision\t= 0.4454786707331564\n",
      "\t\t召回率 Recall   \t= 0.08625\n",
      "TP 2 P 4800 FP 2 N 2608\n",
      "测试特征86_312\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 414 P 4800 FP 280 N 2608\n",
      "测试特征86_313\n",
      "\t\t精确率 Precision\t= 0.4454786707331564\n",
      "\t\t召回率 Recall   \t= 0.08625\n",
      "TP 414 P 4800 FP 280 N 2608\n",
      "测试特征86_323\n",
      "\t\t精确率 Precision\t= 0.4454786707331564\n",
      "\t\t召回率 Recall   \t= 0.08625\n",
      "TP 414 P 4800 FP 280 N 2608\n",
      "测试特征86_3123\n",
      "\t\t精确率 Precision\t= 0.4454786707331564\n",
      "\t\t召回率 Recall   \t= 0.08625\n",
      "TP 2 P 4800 FP 2 N 2608\n",
      "测试特征87_31\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 1 P 4800 FP 0 N 2608\n",
      "测试特征87_32\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00020833333333333335\n",
      "TP 441 P 4800 FP 303 N 2608\n",
      "测试特征87_33\n",
      "\t\t精确率 Precision\t= 0.4415878807983634\n",
      "\t\t召回率 Recall   \t= 0.091875\n",
      "TP 2 P 4800 FP 2 N 2608\n",
      "测试特征87_3all\n",
      "\t\t精确率 Precision\t= 0.35205183585313177\n",
      "\t\t召回率 Recall   \t= 0.0004166666666666667\n",
      "TP 3 P 4800 FP 2 N 2608\n",
      "测试特征87_312\n",
      "\t\t精确率 Precision\t= 0.4490358126721763\n",
      "\t\t召回率 Recall   \t= 0.000625\n",
      "TP 441 P 4800 FP 303 N 2608\n",
      "测试特征87_313\n",
      "\t\t精确率 Precision\t= 0.4415878807983634\n",
      "\t\t召回率 Recall   \t= 0.091875\n",
      "TP 441 P 4800 FP 303 N 2608\n",
      "测试特征87_323\n",
      "\t\t精确率 Precision\t= 0.4415878807983634\n",
      "\t\t召回率 Recall   \t= 0.091875\n",
      "TP 441 P 4800 FP 303 N 2608\n",
      "测试特征87_3123\n",
      "\t\t精确率 Precision\t= 0.4415878807983634\n",
      "\t\t召回率 Recall   \t= 0.091875\n",
      "TP 1505 P 4800 FP 793 N 2608\n",
      "测试特征88_31\n",
      "\t\t精确率 Precision\t= 0.5076725681115032\n",
      "\t\t召回率 Recall   \t= 0.31354166666666666\n",
      "TP 4027 P 4800 FP 2245 N 2608\n",
      "测试特征88_32\n",
      "\t\t精确率 Precision\t= 0.49357132598591924\n",
      "\t\t召回率 Recall   \t= 0.8389583333333334\n",
      "TP 906 P 4800 FP 518 N 2608\n",
      "测试特征88_33\n",
      "\t\t精确率 Precision\t= 0.487260705165007\n",
      "\t\t召回率 Recall   \t= 0.18875\n",
      "TP 1505 P 4800 FP 793 N 2608\n",
      "测试特征88_3all\n",
      "\t\t精确率 Precision\t= 0.5076725681115032\n",
      "\t\t召回率 Recall   \t= 0.31354166666666666\n",
      "TP 4621 P 4800 FP 2506 N 2608\n",
      "测试特征88_312\n",
      "\t\t精确率 Precision\t= 0.5004727502503284\n",
      "\t\t召回率 Recall   \t= 0.9627083333333334\n",
      "TP 1986 P 4800 FP 1027 N 2608\n",
      "测试特征88_313\n",
      "\t\t精确率 Precision\t= 0.5123595719020352\n",
      "\t\t召回率 Recall   \t= 0.41375\n",
      "TP 4276 P 4800 FP 2377 N 2608\n",
      "测试特征88_323\n",
      "\t\t精确率 Precision\t= 0.49428688138612625\n",
      "\t\t召回率 Recall   \t= 0.8908333333333334\n",
      "TP 4740 P 4800 FP 2575 N 2608\n",
      "测试特征88_3123\n",
      "\t\t精确率 Precision\t= 0.5000388319353837\n",
      "\t\t召回率 Recall   \t= 0.9875\n",
      "TP 1467 P 4800 FP 778 N 2608\n",
      "测试特征89_31\n",
      "\t\t精确率 Precision\t= 0.5060536992006703\n",
      "\t\t召回率 Recall   \t= 0.305625\n",
      "TP 4030 P 4800 FP 2244 N 2608\n",
      "测试特征89_32\n",
      "\t\t精确率 Precision\t= 0.4938688359434324\n",
      "\t\t召回率 Recall   \t= 0.8395833333333333\n",
      "TP 1467 P 4800 FP 778 N 2608\n",
      "测试特征89_3all\n",
      "\t\t精确率 Precision\t= 0.5060536992006703\n",
      "\t\t召回率 Recall   \t= 0.305625\n",
      "TP 4600 P 4800 FP 2485 N 2608\n",
      "测试特征89_312\n",
      "\t\t精确率 Precision\t= 0.5014378385608239\n",
      "\t\t召回率 Recall   \t= 0.9583333333333334\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 1467 P 4800 FP 778 N 2608\n",
      "测试特征89_313\n",
      "\t\t精确率 Precision\t= 0.5060536992006703\n",
      "\t\t召回率 Recall   \t= 0.305625\n",
      "TP 4030 P 4800 FP 2244 N 2608\n",
      "测试特征89_323\n",
      "\t\t精确率 Precision\t= 0.4938688359434324\n",
      "\t\t召回率 Recall   \t= 0.8395833333333333\n",
      "TP 4600 P 4800 FP 2485 N 2608\n",
      "测试特征89_3123\n",
      "\t\t精确率 Precision\t= 0.5014378385608239\n",
      "\t\t召回率 Recall   \t= 0.9583333333333334\n",
      "TP 1483 P 4800 FP 782 N 2608\n",
      "测试特征90_31\n",
      "\t\t精确率 Precision\t= 0.5074832731158506\n",
      "\t\t召回率 Recall   \t= 0.30895833333333333\n",
      "TP 3675 P 4800 FP 1985 N 2608\n",
      "测试特征90_32\n",
      "\t\t精确率 Precision\t= 0.5014754818861054\n",
      "\t\t召回率 Recall   \t= 0.765625\n",
      "TP 572 P 4800 FP 337 N 2608\n",
      "测试特征90_33\n",
      "\t\t精确率 Precision\t= 0.4797670014819694\n",
      "\t\t召回率 Recall   \t= 0.11916666666666667\n",
      "TP 1483 P 4800 FP 782 N 2608\n",
      "测试特征90_3all\n",
      "\t\t精确率 Precision\t= 0.5074832731158506\n",
      "\t\t召回率 Recall   \t= 0.30895833333333333\n",
      "TP 4178 P 4800 FP 2216 N 2608\n",
      "测试特征90_312\n",
      "\t\t精确率 Precision\t= 0.5060238636245424\n",
      "\t\t召回率 Recall   \t= 0.8704166666666666\n",
      "TP 1774 P 4800 FP 918 N 2608\n",
      "测试特征90_313\n",
      "\t\t精确率 Precision\t= 0.5121882096209096\n",
      "\t\t召回率 Recall   \t= 0.3695833333333333\n",
      "TP 3958 P 4800 FP 2117 N 2608\n",
      "测试特征90_323\n",
      "\t\t精确率 Precision\t= 0.5039265645723426\n",
      "\t\t召回率 Recall   \t= 0.8245833333333333\n",
      "TP 4361 P 4800 FP 2297 N 2608\n",
      "测试特征90_3123\n",
      "\t\t精确率 Precision\t= 0.5077656733166993\n",
      "\t\t召回率 Recall   \t= 0.9085416666666667\n",
      "TP 1488 P 4800 FP 781 N 2608\n",
      "测试特征91_31\n",
      "\t\t精确率 Precision\t= 0.5086443365125701\n",
      "\t\t召回率 Recall   \t= 0.31\n",
      "TP 3602 P 4800 FP 1940 N 2608\n",
      "测试特征91_32\n",
      "\t\t精确率 Precision\t= 0.5021922359095597\n",
      "\t\t召回率 Recall   \t= 0.7504166666666666\n",
      "TP 868 P 4800 FP 502 N 2608\n",
      "测试特征91_33\n",
      "\t\t精确率 Precision\t= 0.4843949000972323\n",
      "\t\t召回率 Recall   \t= 0.18083333333333335\n",
      "TP 1488 P 4800 FP 781 N 2608\n",
      "测试特征91_3all\n",
      "\t\t精确率 Precision\t= 0.5086443365125701\n",
      "\t\t召回率 Recall   \t= 0.31\n",
      "TP 4285 P 4800 FP 2294 N 2608\n",
      "测试特征91_312\n",
      "\t\t精确率 Precision\t= 0.5036977474570099\n",
      "\t\t召回率 Recall   \t= 0.8927083333333333\n",
      "TP 1793 P 4800 FP 928 N 2608\n",
      "测试特征91_313\n",
      "\t\t精确率 Precision\t= 0.5121429785563708\n",
      "\t\t召回率 Recall   \t= 0.37354166666666666\n",
      "TP 3871 P 4800 FP 2105 N 2608\n",
      "测试特征91_323\n",
      "\t\t精确率 Precision\t= 0.499791282665055\n",
      "\t\t召回率 Recall   \t= 0.8064583333333334\n",
      "TP 4360 P 4800 FP 2336 N 2608\n",
      "测试特征91_3123\n",
      "\t\t精确率 Precision\t= 0.5034998724742823\n",
      "\t\t召回率 Recall   \t= 0.9083333333333333\n",
      "TP 983 P 4800 FP 553 N 2608\n",
      "测试特征92_31\n",
      "\t\t精确率 Precision\t= 0.4913055876662302\n",
      "\t\t召回率 Recall   \t= 0.20479166666666668\n",
      "TP 2708 P 4800 FP 1369 N 2608\n",
      "测试特征92_32\n",
      "\t\t精确率 Precision\t= 0.518016580135758\n",
      "\t\t召回率 Recall   \t= 0.5641666666666667\n",
      "TP 1797 P 4800 FP 1017 N 2608\n",
      "测试特征92_33\n",
      "\t\t精确率 Precision\t= 0.48980871589318586\n",
      "\t\t召回率 Recall   \t= 0.374375\n",
      "TP 983 P 4800 FP 553 N 2608\n",
      "测试特征92_3all\n",
      "\t\t精确率 Precision\t= 0.4913055876662302\n",
      "\t\t召回率 Recall   \t= 0.20479166666666668\n",
      "TP 3205 P 4800 FP 1618 N 2608\n",
      "测试特征92_312\n",
      "\t\t精确率 Precision\t= 0.5183639854536795\n",
      "\t\t召回率 Recall   \t= 0.6677083333333333\n",
      "TP 2414 P 4800 FP 1347 N 2608\n",
      "测试特征92_313\n",
      "\t\t精确率 Precision\t= 0.4933436311250755\n",
      "\t\t召回率 Recall   \t= 0.5029166666666667\n",
      "TP 4066 P 4800 FP 2117 N 2608\n",
      "测试特征92_323\n",
      "\t\t精确率 Precision\t= 0.5106552488793072\n",
      "\t\t召回率 Recall   \t= 0.8470833333333333\n",
      "TP 4361 P 4800 FP 2263 N 2608\n",
      "测试特征92_3123\n",
      "\t\t精确率 Precision\t= 0.5114924126259316\n",
      "\t\t召回率 Recall   \t= 0.9085416666666667\n",
      "TP 962 P 4800 FP 540 N 2608\n",
      "测试特征93_31\n",
      "\t\t精确率 Precision\t= 0.49185398016348497\n",
      "\t\t召回率 Recall   \t= 0.20041666666666666\n",
      "TP 1 P 4800 FP 0 N 2608\n",
      "测试特征93_32\n",
      "\t\t精确率 Precision\t= 1.0\n",
      "\t\t召回率 Recall   \t= 0.00020833333333333335\n",
      "TP 2693 P 4800 FP 1356 N 2608\n",
      "测试特征93_33\n",
      "\t\t精确率 Precision\t= 0.5190119171064097\n",
      "\t\t召回率 Recall   \t= 0.5610416666666667\n",
      "TP 962 P 4800 FP 540 N 2608\n",
      "测试特征93_3all\n",
      "\t\t精确率 Precision\t= 0.49185398016348497\n",
      "\t\t召回率 Recall   \t= 0.20041666666666666\n",
      "TP 962 P 4800 FP 540 N 2608\n",
      "测试特征93_312\n",
      "\t\t精确率 Precision\t= 0.49185398016348497\n",
      "\t\t召回率 Recall   \t= 0.20041666666666666\n",
      "TP 3179 P 4800 FP 1598 N 2608\n",
      "测试特征93_313\n",
      "\t\t精确率 Precision\t= 0.5194355924404833\n",
      "\t\t召回率 Recall   \t= 0.6622916666666666\n",
      "TP 2693 P 4800 FP 1356 N 2608\n",
      "测试特征93_323\n",
      "\t\t精确率 Precision\t= 0.5190119171064097\n",
      "\t\t召回率 Recall   \t= 0.5610416666666667\n",
      "TP 3179 P 4800 FP 1598 N 2608\n",
      "测试特征93_3123\n",
      "\t\t精确率 Precision\t= 0.5194355924404833\n",
      "\t\t召回率 Recall   \t= 0.6622916666666666\n",
      "TP 884 P 4800 FP 494 N 2608\n",
      "测试特征94_31\n",
      "\t\t精确率 Precision\t= 0.49297278064401356\n",
      "\t\t召回率 Recall   \t= 0.18416666666666667\n",
      "TP 2877 P 4800 FP 1469 N 2608\n",
      "测试特征94_32\n",
      "\t\t精确率 Precision\t= 0.5155284829016842\n",
      "\t\t召回率 Recall   \t= 0.599375\n",
      "TP 1889 P 4800 FP 1089 N 2608\n",
      "测试特征94_33\n",
      "\t\t精确率 Precision\t= 0.48519319831013524\n",
      "\t\t召回率 Recall   \t= 0.3935416666666667\n",
      "TP 884 P 4800 FP 494 N 2608\n",
      "测试特征94_3all\n",
      "\t\t精确率 Precision\t= 0.49297278064401356\n",
      "\t\t召回率 Recall   \t= 0.18416666666666667\n",
      "TP 3165 P 4800 FP 1591 N 2608\n",
      "测试特征94_312\n",
      "\t\t精确率 Precision\t= 0.5194297192394243\n",
      "\t\t召回率 Recall   \t= 0.659375\n",
      "TP 2408 P 4800 FP 1370 N 2608\n",
      "测试特征94_313\n",
      "\t\t精确率 Precision\t= 0.4884904119954599\n",
      "\t\t召回率 Recall   \t= 0.5016666666666667\n",
      "TP 4185 P 4800 FP 2209 N 2608\n",
      "测试特征94_323\n",
      "\t\t精确率 Precision\t= 0.5072331217863636\n",
      "\t\t召回率 Recall   \t= 0.871875\n",
      "TP 4351 P 4800 FP 2275 N 2608\n",
      "测试特征94_3123\n",
      "\t\t精确率 Precision\t= 0.5095971655075436\n",
      "\t\t召回率 Recall   \t= 0.9064583333333334\n",
      "TP 925 P 4800 FP 518 N 2608\n",
      "测试特征95_31\n",
      "\t\t精确率 Precision\t= 0.49244712990936557\n",
      "\t\t召回率 Recall   \t= 0.19270833333333334\n",
      "TP 2825 P 4800 FP 1442 N 2608\n",
      "测试特征95_32\n",
      "\t\t精确率 Precision\t= 0.5156061920891303\n",
      "\t\t召回率 Recall   \t= 0.5885416666666666\n",
      "TP 1952 P 4800 FP 1135 N 2608\n",
      "测试特征95_33\n",
      "\t\t精确率 Precision\t= 0.4830538838518483\n",
      "\t\t召回率 Recall   \t= 0.4066666666666667\n",
      "TP 925 P 4800 FP 518 N 2608\n",
      "测试特征95_3all\n",
      "\t\t精确率 Precision\t= 0.49244712990936557\n",
      "\t\t召回率 Recall   \t= 0.19270833333333334\n",
      "TP 3169 P 4800 FP 1594 N 2608\n",
      "测试特征95_312\n",
      "\t\t精确率 Precision\t= 0.5192747502631322\n",
      "\t\t召回率 Recall   \t= 0.6602083333333333\n",
      "TP 2496 P 4800 FP 1416 N 2608\n",
      "测试特征95_313\n",
      "\t\t精确率 Precision\t= 0.48920697218053794\n",
      "\t\t召回率 Recall   \t= 0.52\n",
      "TP 4201 P 4800 FP 2223 N 2608\n",
      "测试特征95_323\n",
      "\t\t精确率 Precision\t= 0.5066077861123668\n",
      "\t\t召回率 Recall   \t= 0.8752083333333334\n",
      "TP 4397 P 4800 FP 2302 N 2608\n",
      "测试特征95_3123\n",
      "\t\t精确率 Precision\t= 0.5092769117842467\n",
      "\t\t召回率 Recall   \t= 0.9160416666666666\n",
      "TP 3961 P 5538 FP 2066 N 3871\n",
      "测试特征96_31\n",
      "\t\t精确率 Precision\t= 0.5726720822345438\n",
      "\t\t召回率 Recall   \t= 0.7152401589021308\n",
      "TP 1230 P 5538 FP 1291 N 3871\n",
      "测试特征96_32\n",
      "\t\t精确率 Precision\t= 0.39974601389921555\n",
      "\t\t召回率 Recall   \t= 0.2221018418201517\n",
      "TP 389 P 5538 FP 773 N 3871\n",
      "测试特征96_33\n",
      "\t\t精确率 Precision\t= 0.26022099323395936\n",
      "\t\t召回率 Recall   \t= 0.07024196460816179\n",
      "TP 3961 P 5538 FP 2066 N 3871\n",
      "测试特征96_3all\n",
      "\t\t精确率 Precision\t= 0.5726720822345438\n",
      "\t\t召回率 Recall   \t= 0.7152401589021308\n",
      "TP 5191 P 5538 FP 3357 N 3871\n",
      "测试特征96_312\n",
      "\t\t精确率 Precision\t= 0.5194297325450228\n",
      "\t\t召回率 Recall   \t= 0.9373420007222825\n",
      "TP 4320 P 5538 FP 2607 N 3871\n",
      "测试特征96_313\n",
      "\t\t精确率 Precision\t= 0.5366677314835943\n",
      "\t\t召回率 Recall   \t= 0.7800650054171181\n",
      "TP 1578 P 5538 FP 1927 N 3871\n",
      "测试特征96_323\n",
      "\t\t精确率 Precision\t= 0.3640273122479613\n",
      "\t\t召回率 Recall   \t= 0.28494041170097506\n",
      "TP 5509 P 5538 FP 3761 N 3871\n",
      "测试特征96_3123\n",
      "\t\t精确率 Precision\t= 0.5058941484148138\n",
      "\t\t召回率 Recall   \t= 0.9947634525099314\n",
      "TP 3912 P 5538 FP 2027 N 3871\n",
      "测试特征97_31\n",
      "\t\t精确率 Precision\t= 0.5742888263960264\n",
      "\t\t召回率 Recall   \t= 0.7063921993499458\n",
      "TP 1794 P 5538 FP 1855 N 3871\n",
      "测试特征97_32\n",
      "\t\t精确率 Precision\t= 0.40334242405023146\n",
      "\t\t召回率 Recall   \t= 0.323943661971831\n",
      "TP 42 P 5538 FP 256 N 3871\n",
      "测试特征97_33\n",
      "\t\t精确率 Precision\t= 0.10287981471989674\n",
      "\t\t召回率 Recall   \t= 0.007583965330444204\n",
      "TP 3912 P 5538 FP 2027 N 3871\n",
      "测试特征97_3all\n",
      "\t\t精确率 Precision\t= 0.5742888263960264\n",
      "\t\t召回率 Recall   \t= 0.7063921993499458\n",
      "TP 5510 P 5538 FP 3705 N 3871\n",
      "测试特征97_312\n",
      "\t\t精确率 Precision\t= 0.509688989784336\n",
      "\t\t召回率 Recall   \t= 0.9949440231130372\n",
      "TP 3916 P 5538 FP 2080 N 3871\n",
      "测试特征97_313\n",
      "\t\t精确率 Precision\t= 0.56821749977397\n",
      "\t\t召回率 Recall   \t= 0.7071144817623691\n",
      "TP 1830 P 5538 FP 2048 N 3871\n",
      "测试特征97_323\n",
      "\t\t精确率 Precision\t= 0.3844580797073488\n",
      "\t\t召回率 Recall   \t= 0.3304442036836403\n",
      "TP 5511 P 5538 FP 3721 N 3871\n",
      "测试特征97_3123\n",
      "\t\t精确率 Precision\t= 0.508657407768373\n",
      "\t\t召回率 Recall   \t= 0.995124593716143\n",
      "TP 3950 P 5538 FP 2061 N 3871\n",
      "测试特征98_31\n",
      "\t\t精确率 Precision\t= 0.572584502222641\n",
      "\t\t召回率 Recall   \t= 0.7132538822679668\n",
      "TP 1298 P 5538 FP 1318 N 3871\n",
      "测试特征98_32\n",
      "\t\t精确率 Precision\t= 0.40771697197954954\n",
      "\t\t召回率 Recall   \t= 0.23438064283134705\n",
      "TP 351 P 5538 FP 788 N 3871\n",
      "测试特征98_33\n",
      "\t\t精确率 Precision\t= 0.23742801649231607\n",
      "\t\t召回率 Recall   \t= 0.06338028169014084\n",
      "TP 3950 P 5538 FP 2061 N 3871\n",
      "测试特征98_3all\n",
      "\t\t精确率 Precision\t= 0.572584502222641\n",
      "\t\t召回率 Recall   \t= 0.7132538822679668\n",
      "TP 5248 P 5538 FP 3379 N 3871\n",
      "测试特征98_312\n",
      "\t\t精确率 Precision\t= 0.5205251318863859\n",
      "\t\t召回率 Recall   \t= 0.9476345250993138\n",
      "TP 4241 P 5538 FP 2598 N 3871\n",
      "测试特征98_313\n",
      "\t\t精确率 Precision\t= 0.5329363909035117\n",
      "\t\t召回率 Recall   \t= 0.7657999277717588\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 1616 P 5538 FP 1952 N 3871\n",
      "测试特征98_323\n",
      "\t\t精确率 Precision\t= 0.3665558167159975\n",
      "\t\t召回率 Recall   \t= 0.29180209461899603\n",
      "TP 5506 P 5538 FP 3762 N 3871\n",
      "测试特征98_3123\n",
      "\t\t精确率 Precision\t= 0.5056915348274669\n",
      "\t\t召回率 Recall   \t= 0.994221740700614\n",
      "TP 5466 P 5538 FP 3660 N 3871\n",
      "测试特征99_31\n",
      "\t\t精确率 Precision\t= 0.5107391948714064\n",
      "\t\t召回率 Recall   \t= 0.9869989165763814\n",
      "TP 283 P 5538 FP 175 N 3871\n",
      "测试特征99_32\n",
      "\t\t精确率 Precision\t= 0.5305968150426006\n",
      "\t\t召回率 Recall   \t= 0.05110148067894547\n",
      "TP 233 P 5538 FP 846 N 3871\n",
      "测试特征99_33\n",
      "\t\t精确率 Precision\t= 0.16143338277468544\n",
      "\t\t召回率 Recall   \t= 0.04207295052365475\n",
      "TP 5466 P 5538 FP 3660 N 3871\n",
      "测试特征99_3all\n",
      "\t\t精确率 Precision\t= 0.5107391948714064\n",
      "\t\t召回率 Recall   \t= 0.9869989165763814\n",
      "TP 5516 P 5538 FP 3693 N 3871\n",
      "测试特征99_312\n",
      "\t\t精确率 Precision\t= 0.5107716508385387\n",
      "\t\t召回率 Recall   \t= 0.9960274467316721\n",
      "TP 5485 P 5538 FP 3809 N 3871\n",
      "测试特征99_313\n",
      "\t\t精确率 Precision\t= 0.5016324574688441\n",
      "\t\t召回率 Recall   \t= 0.9904297580353918\n",
      "TP 509 P 5538 FP 995 N 3871\n",
      "测试特征99_323\n",
      "\t\t精确率 Precision\t= 0.2633914517309928\n",
      "\t\t召回率 Recall   \t= 0.09191043698085952\n",
      "TP 5534 P 5538 FP 3841 N 3871\n",
      "测试特征99_3123\n",
      "\t\t精确率 Precision\t= 0.5017643873883403\n",
      "\t\t召回率 Recall   \t= 0.9992777175875768\n",
      "TP 3961 P 5538 FP 2071 N 3871\n",
      "测试特征100_31\n",
      "\t\t精确率 Precision\t= 0.5720804415185021\n",
      "\t\t召回率 Recall   \t= 0.7152401589021308\n",
      "TP 1735 P 5538 FP 2248 N 3871\n",
      "测试特征100_32\n",
      "\t\t精确率 Precision\t= 0.3504289897597306\n",
      "\t\t召回率 Recall   \t= 0.31328999638858795\n",
      "TP 20 P 5538 FP 67 N 3871\n",
      "测试特征100_33\n",
      "\t\t精确率 Precision\t= 0.17263293092452942\n",
      "\t\t召回率 Recall   \t= 0.0036114120621162874\n",
      "TP 3961 P 5538 FP 2071 N 3871\n",
      "测试特征100_3all\n",
      "\t\t精确率 Precision\t= 0.5720804415185021\n",
      "\t\t召回率 Recall   \t= 0.7152401589021308\n",
      "TP 5538 P 5538 FP 3860 N 3871\n",
      "测试特征100_312\n",
      "\t\t精确率 Precision\t= 0.5007114215496055\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3962 P 5538 FP 2105 N 3871\n",
      "测试特征100_313\n",
      "\t\t精确率 Precision\t= 0.5681514145604761\n",
      "\t\t召回率 Recall   \t= 0.7154207295052365\n",
      "TP 1735 P 5538 FP 2257 N 3871\n",
      "测试特征100_323\n",
      "\t\t精确率 Precision\t= 0.3495200294804426\n",
      "\t\t召回率 Recall   \t= 0.31328999638858795\n",
      "TP 5538 P 5538 FP 3869 N 3871\n",
      "测试特征100_3123\n",
      "\t\t精确率 Precision\t= 0.5001291989664083\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3914 P 5538 FP 2036 N 3871\n",
      "测试特征101_31\n",
      "\t\t精确率 Precision\t= 0.5733303989009199\n",
      "\t\t召回率 Recall   \t= 0.7067533405561575\n",
      "TP 1942 P 5538 FP 2392 N 3871\n",
      "测试特征101_32\n",
      "\t\t精确率 Precision\t= 0.3620374277524711\n",
      "\t\t召回率 Recall   \t= 0.3506681112314915\n",
      "TP 52 P 5538 FP 131 N 3871\n",
      "测试特征101_33\n",
      "\t\t精确率 Precision\t= 0.217197362883995\n",
      "\t\t召回率 Recall   \t= 0.009389671361502348\n",
      "TP 3914 P 5538 FP 2036 N 3871\n",
      "测试特征101_3all\n",
      "\t\t精确率 Precision\t= 0.5733303989009199\n",
      "\t\t召回率 Recall   \t= 0.7067533405561575\n",
      "TP 5520 P 5538 FP 3841 N 3871\n",
      "测试特征101_312\n",
      "\t\t精确率 Precision\t= 0.5011311375133098\n",
      "\t\t召回率 Recall   \t= 0.9967497291440953\n",
      "TP 3938 P 5538 FP 2100 N 3871\n",
      "测试特征101_313\n",
      "\t\t精确率 Precision\t= 0.5672439005458031\n",
      "\t\t召回率 Recall   \t= 0.711087035030697\n",
      "TP 1958 P 5538 FP 2419 N 3871\n",
      "测试特征101_323\n",
      "\t\t精确率 Precision\t= 0.3613403801707107\n",
      "\t\t召回率 Recall   \t= 0.35355724088118456\n",
      "TP 5535 P 5538 FP 3868 N 3871\n",
      "测试特征101_3123\n",
      "\t\t精确率 Precision\t= 0.5000583588538083\n",
      "\t\t召回率 Recall   \t= 0.9994582881906826\n",
      "TP 3950 P 5538 FP 2066 N 3871\n",
      "测试特征102_31\n",
      "\t\t精确率 Precision\t= 0.5719913969638887\n",
      "\t\t召回率 Recall   \t= 0.7132538822679668\n",
      "TP 1778 P 5538 FP 2273 N 3871\n",
      "测试特征102_32\n",
      "\t\t精确率 Precision\t= 0.35349034478394814\n",
      "\t\t召回率 Recall   \t= 0.32105453232213793\n",
      "TP 20 P 5538 FP 67 N 3871\n",
      "测试特征102_33\n",
      "\t\t精确率 Precision\t= 0.17263293092452942\n",
      "\t\t召回率 Recall   \t= 0.0036114120621162874\n",
      "TP 3950 P 5538 FP 2066 N 3871\n",
      "测试特征102_3all\n",
      "\t\t精确率 Precision\t= 0.5719913969638887\n",
      "\t\t召回率 Recall   \t= 0.7132538822679668\n",
      "TP 5538 P 5538 FP 3859 N 3871\n",
      "测试特征102_312\n",
      "\t\t精确率 Precision\t= 0.5007761966364812\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3952 P 5538 FP 2100 N 3871\n",
      "测试特征102_313\n",
      "\t\t精确率 Precision\t= 0.5681148449539052\n",
      "\t\t召回率 Recall   \t= 0.7136150234741784\n",
      "TP 1778 P 5538 FP 2282 N 3871\n",
      "测试特征102_323\n",
      "\t\t精确率 Precision\t= 0.35258776557023497\n",
      "\t\t召回率 Recall   \t= 0.32105453232213793\n",
      "TP 5538 P 5538 FP 3868 N 3871\n",
      "测试特征102_3123\n",
      "\t\t精确率 Precision\t= 0.5001938234914072\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 5466 P 5538 FP 3664 N 3871\n",
      "测试特征103_31\n",
      "\t\t精确率 Precision\t= 0.5104662428222762\n",
      "\t\t召回率 Recall   \t= 0.9869989165763814\n",
      "TP 595 P 5538 FP 1144 N 3871\n",
      "测试特征103_32\n",
      "\t\t精确率 Precision\t= 0.26661887407586105\n",
      "\t\t召回率 Recall   \t= 0.10743950884795955\n",
      "TP 5 P 5538 FP 20 N 3871\n",
      "测试特征103_33\n",
      "\t\t精确率 Precision\t= 0.14875302616915806\n",
      "\t\t召回率 Recall   \t= 0.0009028530155290719\n",
      "TP 5466 P 5538 FP 3664 N 3871\n",
      "测试特征103_3all\n",
      "\t\t精确率 Precision\t= 0.5104662428222762\n",
      "\t\t召回率 Recall   \t= 0.9869989165763814\n",
      "TP 5538 P 5538 FP 3859 N 3871\n",
      "测试特征103_312\n",
      "\t\t精确率 Precision\t= 0.5007761966364812\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 5466 P 5538 FP 3664 N 3871\n",
      "测试特征103_313\n",
      "\t\t精确率 Precision\t= 0.5104662428222762\n",
      "\t\t召回率 Recall   \t= 0.9869989165763814\n",
      "TP 595 P 5538 FP 1144 N 3871\n",
      "测试特征103_323\n",
      "\t\t精确率 Precision\t= 0.26661887407586105\n",
      "\t\t召回率 Recall   \t= 0.10743950884795955\n",
      "TP 5538 P 5538 FP 3859 N 3871\n",
      "测试特征103_3123\n",
      "\t\t精确率 Precision\t= 0.5007761966364812\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 2791 P 4322 FP 1634 N 2784\n",
      "测试特征104_31\n",
      "\t\t精确率 Precision\t= 0.5238667092044843\n",
      "\t\t召回率 Recall   \t= 0.6457658491439149\n",
      "TP 1690 P 4322 FP 1251 N 2784\n",
      "测试特征104_32\n",
      "\t\t精确率 Precision\t= 0.46529484120603076\n",
      "\t\t召回率 Recall   \t= 0.3910226746876446\n",
      "TP 140 P 4322 FP 112 N 2784\n",
      "测试特征104_33\n",
      "\t\t精确率 Precision\t= 0.44603947705716485\n",
      "\t\t召回率 Recall   \t= 0.03239241092086997\n",
      "TP 2791 P 4322 FP 1634 N 2784\n",
      "测试特征104_3all\n",
      "\t\t精确率 Precision\t= 0.5238667092044843\n",
      "\t\t召回率 Recall   \t= 0.6457658491439149\n",
      "TP 4287 P 4322 FP 2756 N 2784\n",
      "测试特征104_312\n",
      "\t\t精确率 Precision\t= 0.5004943295519163\n",
      "\t\t召回率 Recall   \t= 0.9919018972697825\n",
      "TP 2846 P 4322 FP 1674 N 2784\n",
      "测试特征104_313\n",
      "\t\t精确率 Precision\t= 0.5227016341946704\n",
      "\t\t召回率 Recall   \t= 0.6584914391485424\n",
      "TP 1796 P 4322 FP 1332 N 2784\n",
      "测试特征104_323\n",
      "\t\t精确率 Precision\t= 0.46482094210933794\n",
      "\t\t召回率 Recall   \t= 0.4155483572420176\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征104_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 2750 P 4322 FP 1606 N 2784\n",
      "测试特征105_31\n",
      "\t\t精确率 Precision\t= 0.5244865909275876\n",
      "\t\t召回率 Recall   \t= 0.6362795002313744\n",
      "TP 1871 P 4322 FP 1399 N 2784\n",
      "测试特征105_32\n",
      "\t\t精确率 Precision\t= 0.46279037989249905\n",
      "\t\t召回率 Recall   \t= 0.43290143452105506\n",
      "TP 78 P 4322 FP 68 N 2784\n",
      "测试特征105_33\n",
      "\t\t精确率 Precision\t= 0.424915076470312\n",
      "\t\t召回率 Recall   \t= 0.018047200370198982\n",
      "TP 2750 P 4322 FP 1606 N 2784\n",
      "测试特征105_3all\n",
      "\t\t精确率 Precision\t= 0.5244865909275876\n",
      "\t\t召回率 Recall   \t= 0.6362795002313744\n",
      "TP 4268 P 4322 FP 2742 N 2784\n",
      "测试特征105_312\n",
      "\t\t精确率 Precision\t= 0.5006570587934893\n",
      "\t\t召回率 Recall   \t= 0.987505784359093\n",
      "TP 2774 P 4322 FP 1623 N 2784\n",
      "测试特征105_313\n",
      "\t\t精确率 Precision\t= 0.5240276080850503\n",
      "\t\t召回率 Recall   \t= 0.6418324849606664\n",
      "TP 1885 P 4322 FP 1410 N 2784\n",
      "测试特征105_323\n",
      "\t\t精确率 Precision\t= 0.4626965947384291\n",
      "\t\t召回率 Recall   \t= 0.43614067561314207\n",
      "TP 4282 P 4322 FP 2753 N 2784\n",
      "测试特征105_3123\n",
      "\t\t精确率 Precision\t= 0.5004748619558536\n",
      "\t\t召回率 Recall   \t= 0.99074502545118\n",
      "TP 4205 P 4322 FP 2689 N 2784\n",
      "测试特征106_31\n",
      "\t\t精确率 Precision\t= 0.5018188421085932\n",
      "\t\t召回率 Recall   \t= 0.9729291994447016\n",
      "TP 304 P 4322 FP 237 N 2784\n",
      "测试特征106_32\n",
      "\t\t精确率 Precision\t= 0.45242883489696095\n",
      "\t\t召回率 Recall   \t= 0.07033780657103193\n",
      "TP 282 P 4322 FP 191 N 2784\n",
      "测试特征106_33\n",
      "\t\t精确率 Precision\t= 0.4874536660478458\n",
      "\t\t召回率 Recall   \t= 0.06524757056918093\n",
      "TP 4205 P 4322 FP 2689 N 2784\n",
      "测试特征106_3all\n",
      "\t\t精确率 Precision\t= 0.5018188421085932\n",
      "\t\t召回率 Recall   \t= 0.9729291994447016\n",
      "TP 4265 P 4322 FP 2733 N 2784\n",
      "测试特征106_312\n",
      "\t\t精确率 Precision\t= 0.5013031866453577\n",
      "\t\t召回率 Recall   \t= 0.9868116612679315\n",
      "TP 4241 P 4322 FP 2721 N 2784\n",
      "测试特征106_313\n",
      "\t\t精确率 Precision\t= 0.5009925274660367\n",
      "\t\t召回率 Recall   \t= 0.9812586765386395\n",
      "TP 549 P 4322 FP 394 N 2784\n",
      "测试特征106_323\n",
      "\t\t精确率 Precision\t= 0.47300577726996457\n",
      "\t\t召回率 Recall   \t= 0.12702452568255437\n",
      "TP 4298 P 4322 FP 2762 N 2784\n",
      "测试特征106_3123\n",
      "\t\t精确率 Precision\t= 0.500591306629512\n",
      "\t\t召回率 Recall   \t= 0.994447015270708\n",
      "TP 4224 P 4322 FP 2701 N 2784\n",
      "测试特征107_31\n",
      "\t\t精确率 Precision\t= 0.5018327307872229\n",
      "\t\t召回率 Recall   \t= 0.977325312355391\n",
      "TP 316 P 4322 FP 219 N 2784\n",
      "测试特征107_32\n",
      "\t\t精确率 Precision\t= 0.48171839527953825\n",
      "\t\t召回率 Recall   \t= 0.07311429893567793\n",
      "TP 221 P 4322 FP 143 N 2784\n",
      "测试特征107_33\n",
      "\t\t精确率 Precision\t= 0.4988721408242858\n",
      "\t\t召回率 Recall   \t= 0.05113373438223045\n",
      "TP 4224 P 4322 FP 2701 N 2784\n",
      "测试特征107_3all\n",
      "\t\t精确率 Precision\t= 0.5018327307872229\n",
      "\t\t召回率 Recall   \t= 0.977325312355391\n",
      "TP 4283 P 4322 FP 2749 N 2784\n",
      "测试特征107_312\n",
      "\t\t精确率 Precision\t= 0.5008967424979154\n",
      "\t\t召回率 Recall   \t= 0.9909763998149005\n",
      "TP 4251 P 4322 FP 2723 N 2784\n",
      "测试特征107_313\n",
      "\t\t精确率 Precision\t= 0.5013976263780213\n",
      "\t\t召回率 Recall   \t= 0.9835724201758446\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 511 P 4322 FP 342 N 2784\n",
      "测试特征107_323\n",
      "\t\t精确率 Precision\t= 0.49043350197948943\n",
      "\t\t召回率 Recall   \t= 0.11823229986117538\n",
      "TP 4309 P 4322 FP 2769 N 2784\n",
      "测试特征107_3123\n",
      "\t\t精确率 Precision\t= 0.5005975244236387\n",
      "\t\t召回率 Recall   \t= 0.9969921332716335\n",
      "TP 3896 P 4322 FP 2487 N 2784\n",
      "测试特征108_31\n",
      "\t\t精确率 Precision\t= 0.5022609109269166\n",
      "\t\t召回率 Recall   \t= 0.901434521055067\n",
      "TP 1790 P 4322 FP 1109 N 2784\n",
      "测试特征108_32\n",
      "\t\t精确率 Precision\t= 0.5097306202307625\n",
      "\t\t召回率 Recall   \t= 0.4141601110596946\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征108_33\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3896 P 4322 FP 2487 N 2784\n",
      "测试特征108_3all\n",
      "\t\t精确率 Precision\t= 0.5022609109269166\n",
      "\t\t召回率 Recall   \t= 0.901434521055067\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征108_312\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征108_313\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征108_323\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征108_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3861 P 4322 FP 2449 N 2784\n",
      "测试特征109_31\n",
      "\t\t精确率 Precision\t= 0.5038541545867407\n",
      "\t\t召回率 Recall   \t= 0.8933364183248496\n",
      "TP 1868 P 4322 FP 1175 N 2784\n",
      "测试特征109_32\n",
      "\t\t精确率 Precision\t= 0.505942389342322\n",
      "\t\t召回率 Recall   \t= 0.43220731142989355\n",
      "TP 61 P 4322 FP 56 N 2784\n",
      "测试特征109_33\n",
      "\t\t精确率 Precision\t= 0.41233829299561015\n",
      "\t\t召回率 Recall   \t= 0.014113836186950486\n",
      "TP 3861 P 4322 FP 2449 N 2784\n",
      "测试特征109_3all\n",
      "\t\t精确率 Precision\t= 0.5038541545867407\n",
      "\t\t召回率 Recall   \t= 0.8933364183248496\n",
      "TP 4313 P 4322 FP 2773 N 2784\n",
      "测试特征109_312\n",
      "\t\t精确率 Precision\t= 0.500468608717681\n",
      "\t\t召回率 Recall   \t= 0.9979176307265155\n",
      "TP 3875 P 4322 FP 2464 N 2784\n",
      "测试特征109_313\n",
      "\t\t精确率 Precision\t= 0.5032324803446386\n",
      "\t\t召回率 Recall   \t= 0.8965756594169366\n",
      "TP 1882 P 4322 FP 1188 N 2784\n",
      "测试特征109_323\n",
      "\t\t精确率 Precision\t= 0.5050584035664463\n",
      "\t\t召回率 Recall   \t= 0.43544655252198056\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征109_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3890 P 4322 FP 2483 N 2784\n",
      "测试特征110_31\n",
      "\t\t精确率 Precision\t= 0.5022780181107935\n",
      "\t\t召回率 Recall   \t= 0.9000462748727441\n",
      "TP 450 P 4322 FP 254 N 2784\n",
      "测试特征110_32\n",
      "\t\t精确率 Precision\t= 0.5329730263236262\n",
      "\t\t召回率 Recall   \t= 0.10411846367422489\n",
      "TP 1438 P 4322 FP 952 N 2784\n",
      "测试特征110_33\n",
      "\t\t精确率 Precision\t= 0.49315392484000864\n",
      "\t\t召回率 Recall   \t= 0.3327163350300787\n",
      "TP 3890 P 4322 FP 2483 N 2784\n",
      "测试特征110_3all\n",
      "\t\t精确率 Precision\t= 0.5022780181107935\n",
      "\t\t召回率 Recall   \t= 0.9000462748727441\n",
      "TP 3927 P 4322 FP 2503 N 2784\n",
      "测试特征110_312\n",
      "\t\t精确率 Precision\t= 0.5026390373768537\n",
      "\t\t召回率 Recall   \t= 0.9086071263304026\n",
      "TP 4282 P 4322 FP 2764 N 2784\n",
      "测试特征110_313\n",
      "\t\t精确率 Precision\t= 0.49947794235209847\n",
      "\t\t召回率 Recall   \t= 0.99074502545118\n",
      "TP 1802 P 4322 FP 1111 N 2784\n",
      "测试特征110_323\n",
      "\t\t精确率 Precision\t= 0.5109500321331851\n",
      "\t\t召回率 Recall   \t= 0.4169366034243406\n",
      "TP 4318 P 4322 FP 2781 N 2784\n",
      "测试特征110_3123\n",
      "\t\t精确率 Precision\t= 0.5000380603069932\n",
      "\t\t召回率 Recall   \t= 0.999074502545118\n",
      "TP 3841 P 4322 FP 2449 N 2784\n",
      "测试特征111_31\n",
      "\t\t精确率 Precision\t= 0.5025558416841645\n",
      "\t\t召回率 Recall   \t= 0.8887089310504396\n",
      "TP 1927 P 4322 FP 1190 N 2784\n",
      "测试特征111_32\n",
      "\t\t精确率 Precision\t= 0.5105438283478373\n",
      "\t\t召回率 Recall   \t= 0.44585839888940304\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征111_33\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 3841 P 4322 FP 2449 N 2784\n",
      "测试特征111_3all\n",
      "\t\t精确率 Precision\t= 0.5025558416841645\n",
      "\t\t召回率 Recall   \t= 0.8887089310504396\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征111_312\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征111_313\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征111_323\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征111_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 2857 P 4322 FP 1677 N 2784\n",
      "测试特征112_31\n",
      "\t\t精确率 Precision\t= 0.5232173226972818\n",
      "\t\t召回率 Recall   \t= 0.6610365571494679\n",
      "TP 1465 P 4322 FP 1107 N 2784\n",
      "测试特征112_32\n",
      "\t\t精确率 Precision\t= 0.4601775423123556\n",
      "\t\t召回率 Recall   \t= 0.33896344285053215\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征112_33\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 2857 P 4322 FP 1677 N 2784\n",
      "测试特征112_3all\n",
      "\t\t精确率 Precision\t= 0.5232173226972818\n",
      "\t\t召回率 Recall   \t= 0.6610365571494679\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征112_312\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征112_313\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征112_323\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征112_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 2818 P 4322 FP 1648 N 2784\n",
      "测试特征113_31\n",
      "\t\t精确率 Precision\t= 0.5241400836773569\n",
      "\t\t召回率 Recall   \t= 0.6520129569643683\n",
      "TP 1571 P 4322 FP 1214 N 2784\n",
      "测试特征113_32\n",
      "\t\t精确率 Precision\t= 0.45461579623332166\n",
      "\t\t召回率 Recall   \t= 0.36348912540490513\n",
      "TP 31 P 4322 FP 29 N 2784\n",
      "测试特征113_33\n",
      "\t\t精确率 Precision\t= 0.4077829542340367\n",
      "\t\t召回率 Recall   \t= 0.0071726052753354926\n",
      "TP 2818 P 4322 FP 1648 N 2784\n",
      "测试特征113_3all\n",
      "\t\t精确率 Precision\t= 0.5241400836773569\n",
      "\t\t召回率 Recall   \t= 0.6520129569643683\n",
      "TP 4274 P 4322 FP 2750 N 2784\n",
      "测试特征113_312\n",
      "\t\t精确率 Precision\t= 0.5002799323722406\n",
      "\t\t召回率 Recall   \t= 0.988894030541416\n",
      "TP 2843 P 4322 FP 1671 N 2784\n",
      "测试特征113_313\n",
      "\t\t精确率 Precision\t= 0.5228860140738829\n",
      "\t\t召回率 Recall   \t= 0.6577973160573808\n",
      "TP 1597 P 4322 FP 1234 N 2784\n",
      "测试特征113_323\n",
      "\t\t精确率 Precision\t= 0.454634212583272\n",
      "\t\t召回率 Recall   \t= 0.36950485886163814\n",
      "TP 4299 P 4322 FP 2770 N 2784\n",
      "测试特征113_3123\n",
      "\t\t精确率 Precision\t= 0.49992640042612563\n",
      "\t\t召回率 Recall   \t= 0.9946783896344285\n",
      "TP 4247 P 4322 FP 2724 N 2784\n",
      "测试特征114_31\n",
      "\t\t精确率 Precision\t= 0.5010704852222184\n",
      "\t\t召回率 Recall   \t= 0.9826469227209625\n",
      "TP 316 P 4322 FP 247 N 2784\n",
      "测试特征114_32\n",
      "\t\t精确率 Precision\t= 0.45178140974221453\n",
      "\t\t召回率 Recall   \t= 0.07311429893567793\n",
      "TP 4247 P 4322 FP 2724 N 2784\n",
      "测试特征114_3all\n",
      "\t\t精确率 Precision\t= 0.5010704852222184\n",
      "\t\t召回率 Recall   \t= 0.9826469227209625\n",
      "TP 4307 P 4322 FP 2771 N 2784\n",
      "测试特征114_312\n",
      "\t\t精确率 Precision\t= 0.5003009561083075\n",
      "\t\t召回率 Recall   \t= 0.9965293845441925\n",
      "TP 4247 P 4322 FP 2724 N 2784\n",
      "测试特征114_313\n",
      "\t\t精确率 Precision\t= 0.5010704852222184\n",
      "\t\t召回率 Recall   \t= 0.9826469227209625\n",
      "TP 316 P 4322 FP 247 N 2784\n",
      "测试特征114_323\n",
      "\t\t精确率 Precision\t= 0.45178140974221453\n",
      "\t\t召回率 Recall   \t= 0.07311429893567793\n",
      "TP 4307 P 4322 FP 2771 N 2784\n",
      "测试特征114_3123\n",
      "\t\t精确率 Precision\t= 0.5003009561083075\n",
      "\t\t召回率 Recall   \t= 0.9965293845441925\n",
      "TP 4266 P 4322 FP 2738 N 2784\n",
      "测试特征115_31\n",
      "\t\t精确率 Precision\t= 0.5009048434048159\n",
      "\t\t召回率 Recall   \t= 0.987043035631652\n",
      "TP 312 P 4322 FP 231 N 2784\n",
      "测试特征115_32\n",
      "\t\t精确率 Precision\t= 0.4652451271833272\n",
      "\t\t召回率 Recall   \t= 0.07218880148079593\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征115_33\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4266 P 4322 FP 2738 N 2784\n",
      "测试特征115_3all\n",
      "\t\t精确率 Precision\t= 0.5009048434048159\n",
      "\t\t召回率 Recall   \t= 0.987043035631652\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征115_312\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征115_313\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征115_323\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4322 P 4322 FP 2784 N 2784\n",
      "测试特征115_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "false\n",
      "false\n",
      "false\n",
      "false\n",
      "TP 3807 P 5538 FP 2188 N 3871\n",
      "测试特征120_31\n",
      "\t\t精确率 Precision\t= 0.5487776308973387\n",
      "\t\t召回率 Recall   \t= 0.6874322860238353\n",
      "TP 3907 P 5538 FP 2777 N 3871\n",
      "测试特征120_32\n",
      "\t\t精确率 Precision\t= 0.49581961105953337\n",
      "\t\t召回率 Recall   \t= 0.7054893463344167\n",
      "TP 2222 P 5538 FP 1407 N 3871\n",
      "测试特征120_33\n",
      "\t\t精确率 Precision\t= 0.5246867506097602\n",
      "\t\t召回率 Recall   \t= 0.40122788010111954\n",
      "TP 3807 P 5538 FP 2188 N 3871\n",
      "测试特征120_3all\n",
      "\t\t精确率 Precision\t= 0.5487776308973387\n",
      "\t\t召回率 Recall   \t= 0.6874322860238353\n",
      "TP 5427 P 5538 FP 3710 N 3871\n",
      "测试特征120_312\n",
      "\t\t精确率 Precision\t= 0.5055582873490783\n",
      "\t\t召回率 Recall   \t= 0.9799566630552546\n",
      "TP 4386 P 5538 FP 2670 N 3871\n",
      "测试特征120_313\n",
      "\t\t精确率 Precision\t= 0.5344997488719069\n",
      "\t\t召回率 Recall   \t= 0.7919826652221018\n",
      "TP 4466 P 5538 FP 3149 N 3871\n",
      "测试特征120_323\n",
      "\t\t精确率 Precision\t= 0.49782192831363037\n",
      "\t\t召回率 Recall   \t= 0.806428313470567\n",
      "TP 5526 P 5538 FP 3852 N 3871\n",
      "测试特征120_3123\n",
      "\t\t精确率 Precision\t= 0.5006877944191961\n",
      "\t\t召回率 Recall   \t= 0.9978331527627302\n",
      "TP 3761 P 5538 FP 2155 N 3871\n",
      "测试特征121_31\n",
      "\t\t精确率 Precision\t= 0.5495304251604589\n",
      "\t\t召回率 Recall   \t= 0.6791260382809678\n",
      "TP 3890 P 5538 FP 2764 N 3871\n",
      "测试特征121_32\n",
      "\t\t精确率 Precision\t= 0.49590251637218397\n",
      "\t\t召回率 Recall   \t= 0.7024196460816179\n",
      "TP 2648 P 5538 FP 1661 N 3871\n",
      "测试特征121_33\n",
      "\t\t精确率 Precision\t= 0.5270396574100934\n",
      "\t\t召回率 Recall   \t= 0.47815095702419647\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 3761 P 5538 FP 2155 N 3871\n",
      "测试特征121_3all\n",
      "\t\t精确率 Precision\t= 0.5495304251604589\n",
      "\t\t召回率 Recall   \t= 0.6791260382809678\n",
      "TP 5403 P 5538 FP 3674 N 3871\n",
      "测试特征121_312\n",
      "\t\t精确率 Precision\t= 0.5068877679926566\n",
      "\t\t召回率 Recall   \t= 0.9756229685807151\n",
      "TP 4492 P 5538 FP 2744 N 3871\n",
      "测试特征121_313\n",
      "\t\t精确率 Precision\t= 0.5336393000860156\n",
      "\t\t召回率 Recall   \t= 0.8111231491513181\n",
      "TP 4567 P 5538 FP 3211 N 3871\n",
      "测试特征121_323\n",
      "\t\t精确率 Precision\t= 0.4985383956487869\n",
      "\t\t召回率 Recall   \t= 0.8246659443842542\n",
      "TP 5517 P 5538 FP 3838 N 3871\n",
      "测试特征121_3123\n",
      "\t\t精确率 Precision\t= 0.5011905686377728\n",
      "\t\t召回率 Recall   \t= 0.9962080173347779\n",
      "TP 3795 P 5538 FP 2180 N 3871\n",
      "测试特征122_31\n",
      "\t\t精确率 Precision\t= 0.5489029093401651\n",
      "\t\t召回率 Recall   \t= 0.6852654387865655\n",
      "TP 4492 P 5538 FP 3136 N 3871\n",
      "测试特征122_32\n",
      "\t\t精确率 Precision\t= 0.5003073452699844\n",
      "\t\t召回率 Recall   \t= 0.8111231491513181\n",
      "TP 1760 P 5538 FP 1069 N 3871\n",
      "测试特征122_33\n",
      "\t\t精确率 Precision\t= 0.5350597757871975\n",
      "\t\t召回率 Recall   \t= 0.3178042614662333\n",
      "TP 3795 P 5538 FP 2180 N 3871\n",
      "测试特征122_3all\n",
      "\t\t精确率 Precision\t= 0.5489029093401651\n",
      "\t\t召回率 Recall   \t= 0.6852654387865655\n",
      "TP 5472 P 5538 FP 3801 N 3871\n",
      "测试特征122_312\n",
      "\t\t精确率 Precision\t= 0.5015648541806519\n",
      "\t\t召回率 Recall   \t= 0.9880823401950163\n",
      "TP 4199 P 5538 FP 2487 N 3871\n",
      "测试特征122_313\n",
      "\t\t精确率 Precision\t= 0.5413177359895575\n",
      "\t\t召回率 Recall   \t= 0.7582159624413145\n",
      "TP 4546 P 5538 FP 3191 N 3871\n",
      "测试特征122_323\n",
      "\t\t精确率 Precision\t= 0.4989482077966677\n",
      "\t\t召回率 Recall   \t= 0.8208739617190322\n",
      "TP 5526 P 5538 FP 3856 N 3871\n",
      "测试特征122_3123\n",
      "\t\t精确率 Precision\t= 0.500428324045142\n",
      "\t\t召回率 Recall   \t= 0.9978331527627302\n",
      "TP 5157 P 5538 FP 3269 N 3871\n",
      "测试特征123_31\n",
      "\t\t精确率 Precision\t= 0.5244181434322159\n",
      "\t\t召回率 Recall   \t= 0.9312026002166848\n",
      "TP 3360 P 5538 FP 2213 N 3871\n",
      "测试特征123_32\n",
      "\t\t精确率 Precision\t= 0.5148634593867174\n",
      "\t\t召回率 Recall   \t= 0.6067172264355363\n",
      "TP 2183 P 5538 FP 1370 N 3871\n",
      "测试特征123_33\n",
      "\t\t精确率 Precision\t= 0.5269161505882511\n",
      "\t\t召回率 Recall   \t= 0.3941856265799928\n",
      "TP 5157 P 5538 FP 3269 N 3871\n",
      "测试特征123_3all\n",
      "\t\t精确率 Precision\t= 0.5244181434322159\n",
      "\t\t召回率 Recall   \t= 0.9312026002166848\n",
      "TP 5427 P 5538 FP 3710 N 3871\n",
      "测试特征123_312\n",
      "\t\t精确率 Precision\t= 0.5055582873490783\n",
      "\t\t召回率 Recall   \t= 0.9799566630552546\n",
      "TP 5289 P 5538 FP 3470 N 3871\n",
      "测试特征123_313\n",
      "\t\t精确率 Precision\t= 0.5158332157361575\n",
      "\t\t召回率 Recall   \t= 0.9550379198266522\n",
      "TP 4170 P 5538 FP 2737 N 3871\n",
      "测试特征123_323\n",
      "\t\t精确率 Precision\t= 0.5157280724825154\n",
      "\t\t召回率 Recall   \t= 0.752979414951246\n",
      "TP 5525 P 5538 FP 3844 N 3871\n",
      "测试特征123_3123\n",
      "\t\t精确率 Precision\t= 0.5011622986920294\n",
      "\t\t召回率 Recall   \t= 0.9976525821596244\n",
      "TP 3388 P 4800 FP 1717 N 2608\n",
      "测试特征124_31\n",
      "\t\t精确率 Precision\t= 0.5174002008724461\n",
      "\t\t召回率 Recall   \t= 0.7058333333333333\n",
      "TP 1144 P 4800 FP 678 N 2608\n",
      "测试特征124_32\n",
      "\t\t精确率 Precision\t= 0.47829031066606476\n",
      "\t\t召回率 Recall   \t= 0.23833333333333334\n",
      "TP 268 P 4800 FP 213 N 2608\n",
      "测试特征124_33\n",
      "\t\t精确率 Precision\t= 0.4060455086258179\n",
      "\t\t召回率 Recall   \t= 0.05583333333333333\n",
      "TP 3388 P 4800 FP 1717 N 2608\n",
      "测试特征124_3all\n",
      "\t\t精确率 Precision\t= 0.5174002008724461\n",
      "\t\t召回率 Recall   \t= 0.7058333333333333\n",
      "TP 4532 P 4800 FP 2395 N 2608\n",
      "测试特征124_312\n",
      "\t\t精确率 Precision\t= 0.5069365145592691\n",
      "\t\t召回率 Recall   \t= 0.9441666666666667\n",
      "TP 3656 P 4800 FP 1930 N 2608\n",
      "测试特征124_313\n",
      "\t\t精确率 Precision\t= 0.5072038456824589\n",
      "\t\t召回率 Recall   \t= 0.7616666666666667\n",
      "TP 1412 P 4800 FP 891 N 2608\n",
      "测试特征124_323\n",
      "\t\t精确率 Precision\t= 0.4626660448361263\n",
      "\t\t召回率 Recall   \t= 0.2941666666666667\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征124_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 1422 P 4800 FP 782 N 2608\n",
      "测试特征125_31\n",
      "\t\t精确率 Precision\t= 0.49698318560162613\n",
      "\t\t召回率 Recall   \t= 0.29625\n",
      "TP 3497 P 4800 FP 1935 N 2608\n",
      "测试特征125_32\n",
      "\t\t精确率 Precision\t= 0.4954415907366379\n",
      "\t\t召回率 Recall   \t= 0.7285416666666666\n",
      "TP 128 P 4800 FP 112 N 2608\n",
      "测试特征125_33\n",
      "\t\t精确率 Precision\t= 0.38307873090481787\n",
      "\t\t召回率 Recall   \t= 0.02666666666666667\n",
      "TP 1422 P 4800 FP 782 N 2608\n",
      "测试特征125_3all\n",
      "\t\t精确率 Precision\t= 0.49698318560162613\n",
      "\t\t召回率 Recall   \t= 0.29625\n",
      "TP 4765 P 4800 FP 2576 N 2608\n",
      "测试特征125_312\n",
      "\t\t精确率 Precision\t= 0.5012568611063605\n",
      "\t\t召回率 Recall   \t= 0.9927083333333333\n",
      "TP 1529 P 4800 FP 858 N 2608\n",
      "测试特征125_313\n",
      "\t\t精确率 Precision\t= 0.49193390798358555\n",
      "\t\t召回率 Recall   \t= 0.31854166666666667\n",
      "TP 3534 P 4800 FP 1969 N 2608\n",
      "测试特征125_323\n",
      "\t\t精确率 Precision\t= 0.4937184056115233\n",
      "\t\t召回率 Recall   \t= 0.73625\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征125_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 1284 P 4800 FP 708 N 2608\n",
      "测试特征126_31\n",
      "\t\t精确率 Precision\t= 0.49631484590649105\n",
      "\t\t召回率 Recall   \t= 0.2675\n",
      "TP 4202 P 4800 FP 2349 N 2608\n",
      "测试特征126_32\n",
      "\t\t精确率 Precision\t= 0.4928851360006218\n",
      "\t\t召回率 Recall   \t= 0.8754166666666666\n",
      "TP 7 P 4800 FP 6 N 2608\n",
      "测试特征126_33\n",
      "\t\t精确率 Precision\t= 0.3879632777966678\n",
      "\t\t召回率 Recall   \t= 0.0014583333333333334\n",
      "TP 1284 P 4800 FP 708 N 2608\n",
      "测试特征126_3all\n",
      "\t\t精确率 Precision\t= 0.49631484590649105\n",
      "\t\t召回率 Recall   \t= 0.2675\n",
      "TP 4797 P 4800 FP 2605 N 2608\n",
      "测试特征126_312\n",
      "\t\t精确率 Precision\t= 0.5001314433632615\n",
      "\t\t召回率 Recall   \t= 0.999375\n",
      "TP 1289 P 4800 FP 712 N 2608\n",
      "测试特征126_313\n",
      "\t\t精确率 Precision\t= 0.49587804780190087\n",
      "\t\t召回率 Recall   \t= 0.2685416666666667\n",
      "TP 4204 P 4800 FP 2351 N 2608\n",
      "测试特征126_323\n",
      "\t\t精确率 Precision\t= 0.49279135192355267\n",
      "\t\t召回率 Recall   \t= 0.8758333333333334\n",
      "TP 4799 P 4800 FP 2607 N 2608\n",
      "测试特征126_3123\n",
      "\t\t精确率 Precision\t= 0.5000437885187143\n",
      "\t\t召回率 Recall   \t= 0.9997916666666666\n",
      "TP 1302 P 4800 FP 713 N 2608\n",
      "测试特征127_31\n",
      "\t\t精确率 Precision\t= 0.49803579223046707\n",
      "\t\t召回率 Recall   \t= 0.27125\n",
      "TP 4056 P 4800 FP 2309 N 2608\n",
      "测试特征127_32\n",
      "\t\t精确率 Precision\t= 0.48833973001001607\n",
      "\t\t召回率 Recall   \t= 0.845\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征127_33\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 1302 P 4800 FP 713 N 2608\n",
      "测试特征127_3all\n",
      "\t\t精确率 Precision\t= 0.49803579223046707\n",
      "\t\t召回率 Recall   \t= 0.27125\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征127_312\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征127_313\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征127_323\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 4800 P 4800 FP 2608 N 2608\n",
      "测试特征127_3123\n",
      "\t\t精确率 Precision\t= 0.5\n",
      "\t\t召回率 Recall   \t= 1.0\n",
      "TP 2933 P 4800 FP 1476 N 2608\n",
      "测试特征128_31\n",
      "\t\t精确率 Precision\t= 0.5191550681468466\n",
      "\t\t召回率 Recall   \t= 0.6110416666666667\n",
      "TP 3115 P 4800 FP 1799 N 2608\n",
      "测试特征128_32\n",
      "\t\t精确率 Precision\t= 0.4847462157917599\n",
      "\t\t召回率 Recall   \t= 0.6489583333333333\n",
      "TP 882 P 4800 FP 534 N 2608\n",
      "测试特征128_33\n",
      "\t\t精确率 Precision\t= 0.4729673713507432\n",
      "\t\t召回率 Recall   \t= 0.18375\n",
      "TP 2933 P 4800 FP 1476 N 2608\n",
      "测试特征128_3all\n",
      "\t\t精确率 Precision\t= 0.5191550681468466\n",
      "\t\t召回率 Recall   \t= 0.6110416666666667\n",
      "TP 4582 P 4800 FP 2487 N 2608\n",
      "测试特征128_312\n",
      "\t\t精确率 Precision\t= 0.5002565363176388\n",
      "\t\t召回率 Recall   \t= 0.9545833333333333\n",
      "TP 3275 P 4800 FP 1655 N 2608\n",
      "测试特征128_313\n",
      "\t\t精确率 Precision\t= 0.5181132167034673\n",
      "\t\t召回率 Recall   \t= 0.6822916666666666\n",
      "TP 3305 P 4800 FP 1904 N 2608\n",
      "测试特征128_323\n",
      "\t\t精确率 Precision\t= 0.4853659964952271\n",
      "\t\t召回率 Recall   \t= 0.6885416666666667\n",
      "TP 4772 P 4800 FP 2591 N 2608\n",
      "测试特征128_3123\n",
      "\t\t精确率 Precision\t= 0.5001723321947406\n",
      "\t\t召回率 Recall   \t= 0.9941666666666666\n",
      "TP 2890 P 4800 FP 1446 N 2608\n",
      "测试特征129_31\n",
      "\t\t精确率 Precision\t= 0.5205941184921591\n",
      "\t\t召回率 Recall   \t= 0.6020833333333333\n",
      "TP 3217 P 4800 FP 1855 N 2608\n",
      "测试特征129_32\n",
      "\t\t精确率 Precision\t= 0.48513744933484193\n",
      "\t\t召回率 Recall   \t= 0.6702083333333333\n",
      "TP 1019 P 4800 FP 607 N 2608\n",
      "测试特征129_33\n",
      "\t\t精确率 Precision\t= 0.4770201925921245\n",
      "\t\t召回率 Recall   \t= 0.21229166666666666\n",
      "TP 2890 P 4800 FP 1446 N 2608\n",
      "测试特征129_3all\n",
      "\t\t精确率 Precision\t= 0.5205941184921591\n",
      "\t\t召回率 Recall   \t= 0.6020833333333333\n",
      "TP 4613 P 4800 FP 2501 N 2608\n",
      "测试特征129_312\n",
      "\t\t精确率 Precision\t= 0.5005388694990545\n",
      "\t\t召回率 Recall   \t= 0.9610416666666667\n",
      "TP 3254 P 4800 FP 1638 N 2608\n",
      "测试特征129_313\n",
      "\t\t精确率 Precision\t= 0.5190849107752773\n",
      "\t\t召回率 Recall   \t= 0.6779166666666666\n",
      "TP 3377 P 4800 FP 1944 N 2608\n",
      "测试特征129_323\n",
      "\t\t精确率 Precision\t= 0.48555596034405646\n",
      "\t\t召回率 Recall   \t= 0.7035416666666666\n",
      "TP 4773 P 4800 FP 2590 N 2608\n",
      "测试特征129_3123\n",
      "\t\t精确率 Precision\t= 0.5003212220715255\n",
      "\t\t召回率 Recall   \t= 0.994375\n",
      "TP 2922 P 4800 FP 1471 N 2608\n",
      "测试特征130_31\n",
      "\t\t精确率 Precision\t= 0.5190641531148035\n",
      "\t\t召回率 Recall   \t= 0.60875\n",
      "TP 3043 P 4800 FP 1725 N 2608\n",
      "测试特征130_32\n",
      "\t\t精确率 Precision\t= 0.4893977261178736\n",
      "\t\t召回率 Recall   \t= 0.6339583333333333\n",
      "TP 1185 P 4800 FP 713 N 2608\n",
      "测试特征130_33\n",
      "\t\t精确率 Precision\t= 0.47451818550318753\n",
      "\t\t召回率 Recall   \t= 0.246875\n",
      "TP 2922 P 4800 FP 1471 N 2608\n",
      "测试特征130_3all\n",
      "\t\t精确率 Precision\t= 0.5190641531148035\n",
      "\t\t召回率 Recall   \t= 0.60875\n",
      "TP 4500 P 4800 FP 2417 N 2608\n",
      "测试特征130_312\n",
      "\t\t精确率 Precision\t= 0.5028794734677088\n",
      "\t\t召回率 Recall   \t= 0.9375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 3400 P 4800 FP 1741 N 2608\n",
      "测试特征130_313\n",
      "\t\t精确率 Precision\t= 0.5148165350673479\n",
      "\t\t召回率 Recall   \t= 0.7083333333333334\n",
      "TP 3323 P 4800 FP 1907 N 2608\n",
      "测试特征130_323\n",
      "\t\t精确率 Precision\t= 0.4863295051218901\n",
      "\t\t召回率 Recall   \t= 0.6922916666666666\n",
      "TP 4770 P 4800 FP 2589 N 2608\n",
      "测试特征130_3123\n",
      "\t\t精确率 Precision\t= 0.5002605825467601\n",
      "\t\t召回率 Recall   \t= 0.99375\n",
      "TP 2933 P 4800 FP 1476 N 2608\n",
      "测试特征131_31\n",
      "\t\t精确率 Precision\t= 0.5191550681468466\n",
      "\t\t召回率 Recall   \t= 0.6110416666666667\n",
      "TP 3056 P 4800 FP 1758 N 2608\n",
      "测试特征131_32\n",
      "\t\t精确率 Precision\t= 0.4857283272616642\n",
      "\t\t召回率 Recall   \t= 0.6366666666666667\n",
      "TP 1106 P 4800 FP 658 N 2608\n",
      "测试特征131_33\n",
      "\t\t精确率 Precision\t= 0.4773325425362346\n",
      "\t\t召回率 Recall   \t= 0.23041666666666666\n",
      "TP 2933 P 4800 FP 1476 N 2608\n",
      "测试特征131_3all\n",
      "\t\t精确率 Precision\t= 0.5191550681468466\n",
      "\t\t召回率 Recall   \t= 0.6110416666666667\n",
      "TP 4531 P 4800 FP 2450 N 2608\n",
      "测试特征131_312\n",
      "\t\t精确率 Precision\t= 0.5012055894833779\n",
      "\t\t召回率 Recall   \t= 0.9439583333333333\n",
      "TP 3364 P 4800 FP 1711 N 2608\n",
      "测试特征131_313\n",
      "\t\t精确率 Precision\t= 0.5164991258741258\n",
      "\t\t召回率 Recall   \t= 0.7008333333333333\n",
      "TP 3303 P 4800 FP 1901 N 2608\n",
      "测试特征131_323\n",
      "\t\t精确率 Precision\t= 0.4856086783579525\n",
      "\t\t召回率 Recall   \t= 0.688125\n",
      "TP 4770 P 4800 FP 2588 N 2608\n",
      "测试特征131_3123\n",
      "\t\t精确率 Precision\t= 0.5003571635422901\n",
      "\t\t召回率 Recall   \t= 0.99375\n",
      "TP 3074 P 4800 FP 1546 N 2608\n",
      "测试特征132_31\n",
      "\t\t精确率 Precision\t= 0.5193094971094312\n",
      "\t\t召回率 Recall   \t= 0.6404166666666666\n",
      "TP 2845 P 4800 FP 1687 N 2608\n",
      "测试特征132_32\n",
      "\t\t精确率 Precision\t= 0.4781586558538308\n",
      "\t\t召回率 Recall   \t= 0.5927083333333333\n",
      "TP 158 P 4800 FP 144 N 2608\n",
      "测试特征132_33\n",
      "\t\t精确率 Precision\t= 0.3734953737274124\n",
      "\t\t召回率 Recall   \t= 0.032916666666666664\n",
      "TP 3074 P 4800 FP 1546 N 2608\n",
      "测试特征132_3all\n",
      "\t\t精确率 Precision\t= 0.5193094971094312\n",
      "\t\t召回率 Recall   \t= 0.6404166666666666\n",
      "TP 4773 P 4800 FP 2570 N 2608\n",
      "测试特征132_312\n",
      "\t\t精确率 Precision\t= 0.5022592009420277\n",
      "\t\t召回率 Recall   \t= 0.994375\n",
      "TP 3123 P 4800 FP 1600 N 2608\n",
      "测试特征132_313\n",
      "\t\t精确率 Precision\t= 0.5146853189275759\n",
      "\t\t召回率 Recall   \t= 0.650625\n",
      "TP 2869 P 4800 FP 1726 N 2608\n",
      "测试特征132_323\n",
      "\t\t精确率 Precision\t= 0.4745531723167253\n",
      "\t\t召回率 Recall   \t= 0.5977083333333333\n",
      "TP 4796 P 4800 FP 2608 N 2608\n",
      "测试特征132_3123\n",
      "\t\t精确率 Precision\t= 0.49979157982492706\n",
      "\t\t召回率 Recall   \t= 0.9991666666666666\n",
      "TP 3038 P 4800 FP 1516 N 2608\n",
      "测试特征133_31\n",
      "\t\t精确率 Precision\t= 0.5212601342745323\n",
      "\t\t召回率 Recall   \t= 0.6329166666666667\n",
      "TP 2924 P 4800 FP 1736 N 2608\n",
      "测试特征133_32\n",
      "\t\t精确率 Precision\t= 0.4778486723640782\n",
      "\t\t召回率 Recall   \t= 0.6091666666666666\n",
      "TP 136 P 4800 FP 138 N 2608\n",
      "测试特征133_33\n",
      "\t\t精确率 Precision\t= 0.3487289202114271\n",
      "\t\t召回率 Recall   \t= 0.028333333333333332\n",
      "TP 3038 P 4800 FP 1516 N 2608\n",
      "测试特征133_3all\n",
      "\t\t精确率 Precision\t= 0.5212601342745323\n",
      "\t\t召回率 Recall   \t= 0.6329166666666667\n",
      "TP 4759 P 4800 FP 2565 N 2608\n",
      "测试特征133_312\n",
      "\t\t精确率 Precision\t= 0.5020116915617677\n",
      "\t\t召回率 Recall   \t= 0.9914583333333333\n",
      "TP 3079 P 4800 FP 1567 N 2608\n",
      "测试特征133_313\n",
      "\t\t精确率 Precision\t= 0.5163465802174332\n",
      "\t\t召回率 Recall   \t= 0.6414583333333334\n",
      "TP 2944 P 4800 FP 1771 N 2608\n",
      "测试特征133_323\n",
      "\t\t精确率 Precision\t= 0.4745701028113911\n",
      "\t\t召回率 Recall   \t= 0.6133333333333333\n",
      "TP 4778 P 4800 FP 2599 N 2608\n",
      "测试特征133_3123\n",
      "\t\t精确率 Precision\t= 0.49971575487932735\n",
      "\t\t召回率 Recall   \t= 0.9954166666666666\n",
      "TP 3062 P 4800 FP 1543 N 2608\n",
      "测试特征134_31\n",
      "\t\t精确率 Precision\t= 0.5188179699502913\n",
      "\t\t召回率 Recall   \t= 0.6379166666666667\n",
      "TP 2862 P 4800 FP 1693 N 2608\n",
      "测试特征134_32\n",
      "\t\t精确率 Precision\t= 0.4787593672452756\n",
      "\t\t召回率 Recall   \t= 0.59625\n",
      "TP 161 P 4800 FP 142 N 2608\n",
      "测试特征134_33\n",
      "\t\t精确率 Precision\t= 0.3812007030489665\n",
      "\t\t召回率 Recall   \t= 0.033541666666666664\n",
      "TP 3062 P 4800 FP 1543 N 2608\n",
      "测试特征134_3all\n",
      "\t\t精确率 Precision\t= 0.5188179699502913\n",
      "\t\t召回率 Recall   \t= 0.6379166666666667\n",
      "TP 4770 P 4800 FP 2569 N 2608\n",
      "测试特征134_312\n",
      "\t\t精确率 Precision\t= 0.502199314046544\n",
      "\t\t召回率 Recall   \t= 0.99375\n",
      "TP 3112 P 4800 FP 1597 N 2608\n",
      "测试特征134_313\n",
      "\t\t精确率 Precision\t= 0.5142727372267214\n",
      "\t\t召回率 Recall   \t= 0.6483333333333333\n",
      "TP 2887 P 4800 FP 1732 N 2608\n",
      "测试特征134_323\n",
      "\t\t精确率 Precision\t= 0.4752474547582714\n",
      "\t\t召回率 Recall   \t= 0.6014583333333333\n",
      "TP 4793 P 4800 FP 2607 N 2608\n",
      "测试特征134_3123\n",
      "\t\t精确率 Precision\t= 0.49973102787011814\n",
      "\t\t召回率 Recall   \t= 0.9985416666666667\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,135):\n",
    "    #for j in range(3,11):\n",
    "        j=3\n",
    "        try:\n",
    "            #if(j==3):\n",
    "                get_PR(i,j,1)\n",
    "                get_PR(i,j,2)\n",
    "                get_PR(i,j,3)\n",
    "                get_PR(i,j,'all')\n",
    "                get_PR(i,j,12)\n",
    "                get_PR(i,j,13)\n",
    "                get_PR(i,j,23)\n",
    "                get_PR(i,j,123)\n",
    "        except:\n",
    "            print(\"false\")\n",
    "            continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 精确率Precision的计算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 召回率Recall的计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Feature_combination</th>\n",
       "      <th>Standard</th>\n",
       "      <th>Poly</th>\n",
       "      <th>user_name_dic</th>\n",
       "      <th>user_name_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feature1</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>user_agent</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feature2</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>user_agent</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>feature3</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>user_agent</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feature4</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>user_agent</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>feature5</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>user_agent</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>feature6</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>os_version</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>feature7</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>os_version</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>feature8</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>os_version</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>feature9</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>os_version</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>feature10</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>os_version</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>feature11</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>ip_1</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>feature12</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>ip_1</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>feature13</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>ip_1</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>feature14</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>ip_1</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>feature15</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>ip_1</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>feature16</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>ip_12</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>feature17</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>ip_12</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>feature18</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>ip_12</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>feature19</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>ip_12</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>feature20</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>ip_12</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>feature21</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>ip_123</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>feature22</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>ip_123</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>feature23</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>ip_123</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>feature24</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>ip_123</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>feature25</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>ip_123</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>feature26</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>ip_1234</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>feature27</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>ip_1234</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>feature28</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>ip_1234</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>feature29</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>ip_1234</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>feature30</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>ip_1234</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>feature31</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>ip_city</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>feature32</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>ip_city</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>feature33</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>ip_city</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>feature34</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>ip_city</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>feature35</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>ip_city</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>feature36</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>resource_owner</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>feature37</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>resource_owner</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>feature38</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>resource_owner</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>feature39</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>resource_owner</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>feature40</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>resource_owner</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>feature41</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>resource_type</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>feature42</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>resource_type</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>feature43</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>resource_type</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>feature44</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>resource_type</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>feature45</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>resource_type</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>feature46</td>\n",
       "      <td>['user_name']</td>\n",
       "      <td>resource_category</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>feature47</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>resource_category</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>feature48</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>resource_category</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>feature49</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>resource_category</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>feature50</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>resource_category</td>\n",
       "      <td>nunique</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>feature51</td>\n",
       "      <td>['user_name', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>feature52</td>\n",
       "      <td>['user_name', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>feature53</td>\n",
       "      <td>['user_name', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>feature54</td>\n",
       "      <td>['user_name', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>feature55</td>\n",
       "      <td>['user_name', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>feature56</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>feature57</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>feature58</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>feature59</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>feature60</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>feature61</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>feature62</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>feature63</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>feature64</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>feature65</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>feature66</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>feature67</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>feature68</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>feature69</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>feature70</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>feature71</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>feature72</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>feature73</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>feature74</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>feature75</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>feature76</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>feature77</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>feature78</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>feature79</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>feature80</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>feature81</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>feature82</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>feature83</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>feature84</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>feature85</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>feature86</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>feature87</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>feature88</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>feature89</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>feature90</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>feature91</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>feature92</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_day']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>feature93</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>feature94</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_3hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>feature95</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_6hour']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>feature96</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>feature97</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>feature98</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>feature99</td>\n",
       "      <td>['user_name', 'user_agent', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>feature100</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>feature101</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>feature102</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>feature103</td>\n",
       "      <td>['user_name', 'os_version', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>feature104</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>feature105</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>feature106</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>feature107</td>\n",
       "      <td>['user_name', 'ip_1', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>feature108</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>feature109</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>feature110</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>feature111</td>\n",
       "      <td>['user_name', 'ip_12', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>feature112</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>feature113</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>feature114</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>feature115</td>\n",
       "      <td>['user_name', 'ip_123', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>feature116</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>feature117</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>feature118</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>feature119</td>\n",
       "      <td>['user_name', 'ip_1234', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...</td>\n",
       "      <td>7106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>feature120</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>feature121</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>feature122</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>feature123</td>\n",
       "      <td>['user_name', 'ip_city', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...</td>\n",
       "      <td>9409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>feature124</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>feature125</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>feature126</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>feature127</td>\n",
       "      <td>['user_name', 'resource_owner', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>feature128</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>feature129</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>feature130</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>feature131</td>\n",
       "      <td>['user_name', 'resource_type', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>feature132</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_day', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>feature133</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>feature134</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_3hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>feature135</td>\n",
       "      <td>['user_name', 'resource_category', 'time_stamp_6hour', 'event_type']</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>count</td>\n",
       "      <td>{1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...</td>\n",
       "      <td>7408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  \\\n",
       "0      feature1   \n",
       "1      feature2   \n",
       "2      feature3   \n",
       "3      feature4   \n",
       "4      feature5   \n",
       "5      feature6   \n",
       "6      feature7   \n",
       "7      feature8   \n",
       "8      feature9   \n",
       "9     feature10   \n",
       "10    feature11   \n",
       "11    feature12   \n",
       "12    feature13   \n",
       "13    feature14   \n",
       "14    feature15   \n",
       "15    feature16   \n",
       "16    feature17   \n",
       "17    feature18   \n",
       "18    feature19   \n",
       "19    feature20   \n",
       "20    feature21   \n",
       "21    feature22   \n",
       "22    feature23   \n",
       "23    feature24   \n",
       "24    feature25   \n",
       "25    feature26   \n",
       "26    feature27   \n",
       "27    feature28   \n",
       "28    feature29   \n",
       "29    feature30   \n",
       "30    feature31   \n",
       "31    feature32   \n",
       "32    feature33   \n",
       "33    feature34   \n",
       "34    feature35   \n",
       "35    feature36   \n",
       "36    feature37   \n",
       "37    feature38   \n",
       "38    feature39   \n",
       "39    feature40   \n",
       "40    feature41   \n",
       "41    feature42   \n",
       "42    feature43   \n",
       "43    feature44   \n",
       "44    feature45   \n",
       "45    feature46   \n",
       "46    feature47   \n",
       "47    feature48   \n",
       "48    feature49   \n",
       "49    feature50   \n",
       "50    feature51   \n",
       "51    feature52   \n",
       "52    feature53   \n",
       "53    feature54   \n",
       "54    feature55   \n",
       "55    feature56   \n",
       "56    feature57   \n",
       "57    feature58   \n",
       "58    feature59   \n",
       "59    feature60   \n",
       "60    feature61   \n",
       "61    feature62   \n",
       "62    feature63   \n",
       "63    feature64   \n",
       "64    feature65   \n",
       "65    feature66   \n",
       "66    feature67   \n",
       "67    feature68   \n",
       "68    feature69   \n",
       "69    feature70   \n",
       "70    feature71   \n",
       "71    feature72   \n",
       "72    feature73   \n",
       "73    feature74   \n",
       "74    feature75   \n",
       "75    feature76   \n",
       "76    feature77   \n",
       "77    feature78   \n",
       "78    feature79   \n",
       "79    feature80   \n",
       "80    feature81   \n",
       "81    feature82   \n",
       "82    feature83   \n",
       "83    feature84   \n",
       "84    feature85   \n",
       "85    feature86   \n",
       "86    feature87   \n",
       "87    feature88   \n",
       "88    feature89   \n",
       "89    feature90   \n",
       "90    feature91   \n",
       "91    feature92   \n",
       "92    feature93   \n",
       "93    feature94   \n",
       "94    feature95   \n",
       "95    feature96   \n",
       "96    feature97   \n",
       "97    feature98   \n",
       "98    feature99   \n",
       "99   feature100   \n",
       "100  feature101   \n",
       "101  feature102   \n",
       "102  feature103   \n",
       "103  feature104   \n",
       "104  feature105   \n",
       "105  feature106   \n",
       "106  feature107   \n",
       "107  feature108   \n",
       "108  feature109   \n",
       "109  feature110   \n",
       "110  feature111   \n",
       "111  feature112   \n",
       "112  feature113   \n",
       "113  feature114   \n",
       "114  feature115   \n",
       "115  feature116   \n",
       "116  feature117   \n",
       "117  feature118   \n",
       "118  feature119   \n",
       "119  feature120   \n",
       "120  feature121   \n",
       "121  feature122   \n",
       "122  feature123   \n",
       "123  feature124   \n",
       "124  feature125   \n",
       "125  feature126   \n",
       "126  feature127   \n",
       "127  feature128   \n",
       "128  feature129   \n",
       "129  feature130   \n",
       "130  feature131   \n",
       "131  feature132   \n",
       "132  feature133   \n",
       "133  feature134   \n",
       "134  feature135   \n",
       "\n",
       "                                                      Feature_combination  \\\n",
       "0                                                           ['user_name']   \n",
       "1                                         ['user_name', 'time_stamp_day']   \n",
       "2                                        ['user_name', 'time_stamp_hour']   \n",
       "3                                       ['user_name', 'time_stamp_3hour']   \n",
       "4                                       ['user_name', 'time_stamp_6hour']   \n",
       "5                                                           ['user_name']   \n",
       "6                                         ['user_name', 'time_stamp_day']   \n",
       "7                                        ['user_name', 'time_stamp_hour']   \n",
       "8                                       ['user_name', 'time_stamp_3hour']   \n",
       "9                                       ['user_name', 'time_stamp_6hour']   \n",
       "10                                                          ['user_name']   \n",
       "11                                        ['user_name', 'time_stamp_day']   \n",
       "12                                       ['user_name', 'time_stamp_hour']   \n",
       "13                                      ['user_name', 'time_stamp_3hour']   \n",
       "14                                      ['user_name', 'time_stamp_6hour']   \n",
       "15                                                          ['user_name']   \n",
       "16                                        ['user_name', 'time_stamp_day']   \n",
       "17                                       ['user_name', 'time_stamp_hour']   \n",
       "18                                      ['user_name', 'time_stamp_3hour']   \n",
       "19                                      ['user_name', 'time_stamp_6hour']   \n",
       "20                                                          ['user_name']   \n",
       "21                                        ['user_name', 'time_stamp_day']   \n",
       "22                                       ['user_name', 'time_stamp_hour']   \n",
       "23                                      ['user_name', 'time_stamp_3hour']   \n",
       "24                                      ['user_name', 'time_stamp_6hour']   \n",
       "25                                                          ['user_name']   \n",
       "26                                        ['user_name', 'time_stamp_day']   \n",
       "27                                       ['user_name', 'time_stamp_hour']   \n",
       "28                                      ['user_name', 'time_stamp_3hour']   \n",
       "29                                      ['user_name', 'time_stamp_6hour']   \n",
       "30                                                          ['user_name']   \n",
       "31                                        ['user_name', 'time_stamp_day']   \n",
       "32                                       ['user_name', 'time_stamp_hour']   \n",
       "33                                      ['user_name', 'time_stamp_3hour']   \n",
       "34                                      ['user_name', 'time_stamp_6hour']   \n",
       "35                                                          ['user_name']   \n",
       "36                                        ['user_name', 'time_stamp_day']   \n",
       "37                                       ['user_name', 'time_stamp_hour']   \n",
       "38                                      ['user_name', 'time_stamp_3hour']   \n",
       "39                                      ['user_name', 'time_stamp_6hour']   \n",
       "40                                                          ['user_name']   \n",
       "41                                        ['user_name', 'time_stamp_day']   \n",
       "42                                       ['user_name', 'time_stamp_hour']   \n",
       "43                                      ['user_name', 'time_stamp_3hour']   \n",
       "44                                      ['user_name', 'time_stamp_6hour']   \n",
       "45                                                          ['user_name']   \n",
       "46                                        ['user_name', 'time_stamp_day']   \n",
       "47                                       ['user_name', 'time_stamp_hour']   \n",
       "48                                      ['user_name', 'time_stamp_3hour']   \n",
       "49                                      ['user_name', 'time_stamp_6hour']   \n",
       "50                                            ['user_name', 'event_type']   \n",
       "51                                        ['user_name', 'time_stamp_day']   \n",
       "52                                       ['user_name', 'time_stamp_hour']   \n",
       "53                                      ['user_name', 'time_stamp_3hour']   \n",
       "54                                      ['user_name', 'time_stamp_6hour']   \n",
       "55                          ['user_name', 'user_agent', 'time_stamp_day']   \n",
       "56                         ['user_name', 'user_agent', 'time_stamp_hour']   \n",
       "57                        ['user_name', 'user_agent', 'time_stamp_3hour']   \n",
       "58                        ['user_name', 'user_agent', 'time_stamp_6hour']   \n",
       "59                          ['user_name', 'os_version', 'time_stamp_day']   \n",
       "60                         ['user_name', 'os_version', 'time_stamp_hour']   \n",
       "61                        ['user_name', 'os_version', 'time_stamp_3hour']   \n",
       "62                        ['user_name', 'os_version', 'time_stamp_6hour']   \n",
       "63                                ['user_name', 'ip_1', 'time_stamp_day']   \n",
       "64                               ['user_name', 'ip_1', 'time_stamp_hour']   \n",
       "65                              ['user_name', 'ip_1', 'time_stamp_3hour']   \n",
       "66                              ['user_name', 'ip_1', 'time_stamp_6hour']   \n",
       "67                               ['user_name', 'ip_12', 'time_stamp_day']   \n",
       "68                              ['user_name', 'ip_12', 'time_stamp_hour']   \n",
       "69                             ['user_name', 'ip_12', 'time_stamp_3hour']   \n",
       "70                             ['user_name', 'ip_12', 'time_stamp_6hour']   \n",
       "71                              ['user_name', 'ip_123', 'time_stamp_day']   \n",
       "72                             ['user_name', 'ip_123', 'time_stamp_hour']   \n",
       "73                            ['user_name', 'ip_123', 'time_stamp_3hour']   \n",
       "74                            ['user_name', 'ip_123', 'time_stamp_6hour']   \n",
       "75                             ['user_name', 'ip_1234', 'time_stamp_day']   \n",
       "76                            ['user_name', 'ip_1234', 'time_stamp_hour']   \n",
       "77                           ['user_name', 'ip_1234', 'time_stamp_3hour']   \n",
       "78                           ['user_name', 'ip_1234', 'time_stamp_6hour']   \n",
       "79                             ['user_name', 'ip_city', 'time_stamp_day']   \n",
       "80                            ['user_name', 'ip_city', 'time_stamp_hour']   \n",
       "81                           ['user_name', 'ip_city', 'time_stamp_3hour']   \n",
       "82                           ['user_name', 'ip_city', 'time_stamp_6hour']   \n",
       "83                      ['user_name', 'resource_owner', 'time_stamp_day']   \n",
       "84                     ['user_name', 'resource_owner', 'time_stamp_hour']   \n",
       "85                    ['user_name', 'resource_owner', 'time_stamp_3hour']   \n",
       "86                    ['user_name', 'resource_owner', 'time_stamp_6hour']   \n",
       "87                       ['user_name', 'resource_type', 'time_stamp_day']   \n",
       "88                      ['user_name', 'resource_type', 'time_stamp_hour']   \n",
       "89                     ['user_name', 'resource_type', 'time_stamp_3hour']   \n",
       "90                     ['user_name', 'resource_type', 'time_stamp_6hour']   \n",
       "91                   ['user_name', 'resource_category', 'time_stamp_day']   \n",
       "92                  ['user_name', 'resource_category', 'time_stamp_hour']   \n",
       "93                 ['user_name', 'resource_category', 'time_stamp_3hour']   \n",
       "94                 ['user_name', 'resource_category', 'time_stamp_6hour']   \n",
       "95            ['user_name', 'user_agent', 'time_stamp_day', 'event_type']   \n",
       "96           ['user_name', 'user_agent', 'time_stamp_hour', 'event_type']   \n",
       "97          ['user_name', 'user_agent', 'time_stamp_3hour', 'event_type']   \n",
       "98          ['user_name', 'user_agent', 'time_stamp_6hour', 'event_type']   \n",
       "99            ['user_name', 'os_version', 'time_stamp_day', 'event_type']   \n",
       "100          ['user_name', 'os_version', 'time_stamp_hour', 'event_type']   \n",
       "101         ['user_name', 'os_version', 'time_stamp_3hour', 'event_type']   \n",
       "102         ['user_name', 'os_version', 'time_stamp_6hour', 'event_type']   \n",
       "103                 ['user_name', 'ip_1', 'time_stamp_day', 'event_type']   \n",
       "104                ['user_name', 'ip_1', 'time_stamp_hour', 'event_type']   \n",
       "105               ['user_name', 'ip_1', 'time_stamp_3hour', 'event_type']   \n",
       "106               ['user_name', 'ip_1', 'time_stamp_6hour', 'event_type']   \n",
       "107                ['user_name', 'ip_12', 'time_stamp_day', 'event_type']   \n",
       "108               ['user_name', 'ip_12', 'time_stamp_hour', 'event_type']   \n",
       "109              ['user_name', 'ip_12', 'time_stamp_3hour', 'event_type']   \n",
       "110              ['user_name', 'ip_12', 'time_stamp_6hour', 'event_type']   \n",
       "111               ['user_name', 'ip_123', 'time_stamp_day', 'event_type']   \n",
       "112              ['user_name', 'ip_123', 'time_stamp_hour', 'event_type']   \n",
       "113             ['user_name', 'ip_123', 'time_stamp_3hour', 'event_type']   \n",
       "114             ['user_name', 'ip_123', 'time_stamp_6hour', 'event_type']   \n",
       "115              ['user_name', 'ip_1234', 'time_stamp_day', 'event_type']   \n",
       "116             ['user_name', 'ip_1234', 'time_stamp_hour', 'event_type']   \n",
       "117            ['user_name', 'ip_1234', 'time_stamp_3hour', 'event_type']   \n",
       "118            ['user_name', 'ip_1234', 'time_stamp_6hour', 'event_type']   \n",
       "119              ['user_name', 'ip_city', 'time_stamp_day', 'event_type']   \n",
       "120             ['user_name', 'ip_city', 'time_stamp_hour', 'event_type']   \n",
       "121            ['user_name', 'ip_city', 'time_stamp_3hour', 'event_type']   \n",
       "122            ['user_name', 'ip_city', 'time_stamp_6hour', 'event_type']   \n",
       "123       ['user_name', 'resource_owner', 'time_stamp_day', 'event_type']   \n",
       "124      ['user_name', 'resource_owner', 'time_stamp_hour', 'event_type']   \n",
       "125     ['user_name', 'resource_owner', 'time_stamp_3hour', 'event_type']   \n",
       "126     ['user_name', 'resource_owner', 'time_stamp_6hour', 'event_type']   \n",
       "127        ['user_name', 'resource_type', 'time_stamp_day', 'event_type']   \n",
       "128       ['user_name', 'resource_type', 'time_stamp_hour', 'event_type']   \n",
       "129      ['user_name', 'resource_type', 'time_stamp_3hour', 'event_type']   \n",
       "130      ['user_name', 'resource_type', 'time_stamp_6hour', 'event_type']   \n",
       "131    ['user_name', 'resource_category', 'time_stamp_day', 'event_type']   \n",
       "132   ['user_name', 'resource_category', 'time_stamp_hour', 'event_type']   \n",
       "133  ['user_name', 'resource_category', 'time_stamp_3hour', 'event_type']   \n",
       "134  ['user_name', 'resource_category', 'time_stamp_6hour', 'event_type']   \n",
       "\n",
       "              Standard     Poly  \\\n",
       "0           user_agent  nunique   \n",
       "1           user_agent  nunique   \n",
       "2           user_agent  nunique   \n",
       "3           user_agent  nunique   \n",
       "4           user_agent  nunique   \n",
       "5           os_version  nunique   \n",
       "6           os_version  nunique   \n",
       "7           os_version  nunique   \n",
       "8           os_version  nunique   \n",
       "9           os_version  nunique   \n",
       "10                ip_1  nunique   \n",
       "11                ip_1  nunique   \n",
       "12                ip_1  nunique   \n",
       "13                ip_1  nunique   \n",
       "14                ip_1  nunique   \n",
       "15               ip_12  nunique   \n",
       "16               ip_12  nunique   \n",
       "17               ip_12  nunique   \n",
       "18               ip_12  nunique   \n",
       "19               ip_12  nunique   \n",
       "20              ip_123  nunique   \n",
       "21              ip_123  nunique   \n",
       "22              ip_123  nunique   \n",
       "23              ip_123  nunique   \n",
       "24              ip_123  nunique   \n",
       "25             ip_1234  nunique   \n",
       "26             ip_1234  nunique   \n",
       "27             ip_1234  nunique   \n",
       "28             ip_1234  nunique   \n",
       "29             ip_1234  nunique   \n",
       "30             ip_city  nunique   \n",
       "31             ip_city  nunique   \n",
       "32             ip_city  nunique   \n",
       "33             ip_city  nunique   \n",
       "34             ip_city  nunique   \n",
       "35      resource_owner  nunique   \n",
       "36      resource_owner  nunique   \n",
       "37      resource_owner  nunique   \n",
       "38      resource_owner  nunique   \n",
       "39      resource_owner  nunique   \n",
       "40       resource_type  nunique   \n",
       "41       resource_type  nunique   \n",
       "42       resource_type  nunique   \n",
       "43       resource_type  nunique   \n",
       "44       resource_type  nunique   \n",
       "45   resource_category  nunique   \n",
       "46   resource_category  nunique   \n",
       "47   resource_category  nunique   \n",
       "48   resource_category  nunique   \n",
       "49   resource_category  nunique   \n",
       "50          Unnamed: 0    count   \n",
       "51          Unnamed: 0    count   \n",
       "52          Unnamed: 0    count   \n",
       "53          Unnamed: 0    count   \n",
       "54          Unnamed: 0    count   \n",
       "55          Unnamed: 0    count   \n",
       "56          Unnamed: 0    count   \n",
       "57          Unnamed: 0    count   \n",
       "58          Unnamed: 0    count   \n",
       "59          Unnamed: 0    count   \n",
       "60          Unnamed: 0    count   \n",
       "61          Unnamed: 0    count   \n",
       "62          Unnamed: 0    count   \n",
       "63          Unnamed: 0    count   \n",
       "64          Unnamed: 0    count   \n",
       "65          Unnamed: 0    count   \n",
       "66          Unnamed: 0    count   \n",
       "67          Unnamed: 0    count   \n",
       "68          Unnamed: 0    count   \n",
       "69          Unnamed: 0    count   \n",
       "70          Unnamed: 0    count   \n",
       "71          Unnamed: 0    count   \n",
       "72          Unnamed: 0    count   \n",
       "73          Unnamed: 0    count   \n",
       "74          Unnamed: 0    count   \n",
       "75          Unnamed: 0    count   \n",
       "76          Unnamed: 0    count   \n",
       "77          Unnamed: 0    count   \n",
       "78          Unnamed: 0    count   \n",
       "79          Unnamed: 0    count   \n",
       "80          Unnamed: 0    count   \n",
       "81          Unnamed: 0    count   \n",
       "82          Unnamed: 0    count   \n",
       "83          Unnamed: 0    count   \n",
       "84          Unnamed: 0    count   \n",
       "85          Unnamed: 0    count   \n",
       "86          Unnamed: 0    count   \n",
       "87          Unnamed: 0    count   \n",
       "88          Unnamed: 0    count   \n",
       "89          Unnamed: 0    count   \n",
       "90          Unnamed: 0    count   \n",
       "91          Unnamed: 0    count   \n",
       "92          Unnamed: 0    count   \n",
       "93          Unnamed: 0    count   \n",
       "94          Unnamed: 0    count   \n",
       "95          Unnamed: 0    count   \n",
       "96          Unnamed: 0    count   \n",
       "97          Unnamed: 0    count   \n",
       "98          Unnamed: 0    count   \n",
       "99          Unnamed: 0    count   \n",
       "100         Unnamed: 0    count   \n",
       "101         Unnamed: 0    count   \n",
       "102         Unnamed: 0    count   \n",
       "103         Unnamed: 0    count   \n",
       "104         Unnamed: 0    count   \n",
       "105         Unnamed: 0    count   \n",
       "106         Unnamed: 0    count   \n",
       "107         Unnamed: 0    count   \n",
       "108         Unnamed: 0    count   \n",
       "109         Unnamed: 0    count   \n",
       "110         Unnamed: 0    count   \n",
       "111         Unnamed: 0    count   \n",
       "112         Unnamed: 0    count   \n",
       "113         Unnamed: 0    count   \n",
       "114         Unnamed: 0    count   \n",
       "115         Unnamed: 0    count   \n",
       "116         Unnamed: 0    count   \n",
       "117         Unnamed: 0    count   \n",
       "118         Unnamed: 0    count   \n",
       "119         Unnamed: 0    count   \n",
       "120         Unnamed: 0    count   \n",
       "121         Unnamed: 0    count   \n",
       "122         Unnamed: 0    count   \n",
       "123         Unnamed: 0    count   \n",
       "124         Unnamed: 0    count   \n",
       "125         Unnamed: 0    count   \n",
       "126         Unnamed: 0    count   \n",
       "127         Unnamed: 0    count   \n",
       "128         Unnamed: 0    count   \n",
       "129         Unnamed: 0    count   \n",
       "130         Unnamed: 0    count   \n",
       "131         Unnamed: 0    count   \n",
       "132         Unnamed: 0    count   \n",
       "133         Unnamed: 0    count   \n",
       "134         Unnamed: 0    count   \n",
       "\n",
       "                                                                                           user_name_dic  \\\n",
       "0    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "1    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "2    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "3    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "4    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "5    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "6    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "7    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "8    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "9    {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "10   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "11   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "12   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "13   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "14   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "15   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "16   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "17   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "18   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "19   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "20   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "21   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "22   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "23   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "24   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "25   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "26   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "27   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "28   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "29   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "30   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "31   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "32   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "33   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "34   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "35   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "36   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "37   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "38   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "39   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "40   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "41   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "42   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "43   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "44   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "45   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "46   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "47   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "48   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "49   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "50   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "51   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "52   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "53   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "54   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "55   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "56   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "57   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "58   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "59   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "60   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "61   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "62   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "63   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "64   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "65   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "66   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "67   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "68   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "69   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "70   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "71   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "72   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "73   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "74   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "75   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "76   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "77   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "78   {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "79   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "80   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "81   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "82   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "83   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "84   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "85   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "86   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "87   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "88   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "89   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "90   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "91   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "92   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "93   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "94   {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "95   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "96   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "97   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "98   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "99   {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "100  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "101  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "102  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "103  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "104  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "105  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "106  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "107  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "108  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "109  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "110  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "111  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "112  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "113  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "114  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "115  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "116  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "117  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "118  {458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 1146929, 1671217, 1671221, 1...   \n",
       "119  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "120  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "121  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "122  {1343494, 458762, 1310734, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 167...   \n",
       "123  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "124  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "125  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "126  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "127  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "128  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "129  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "130  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "131  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "132  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "133  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "134  {1343494, 458762, 1343510, 1540121, 1671194, 1671197, 65569, 32805, 65582, 1146929, 1671224, 655...   \n",
       "\n",
       "     user_name_num  \n",
       "0             9409  \n",
       "1             9409  \n",
       "2             9409  \n",
       "3             9409  \n",
       "4             9409  \n",
       "5             9409  \n",
       "6             9409  \n",
       "7             9409  \n",
       "8             9409  \n",
       "9             9409  \n",
       "10            9409  \n",
       "11            9409  \n",
       "12            9409  \n",
       "13            9409  \n",
       "14            9409  \n",
       "15            9409  \n",
       "16            9409  \n",
       "17            9409  \n",
       "18            9409  \n",
       "19            9409  \n",
       "20            9409  \n",
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      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_row',200) \n",
    "pd.set_option('display.max_colwidth', 100)\n",
    "pd.read_csv(\"/data/csv/feature_list.csv\",sep=',')"
   ]
  }
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